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The PolicyViz Podcast
31 minutes | 12 days ago
Episode #185: Arathi Sethumadhavan
Arathi Sethumadhavan is the Head of User Research for Ethics & Society at Microsoft’s Cloud+AI, where she brings the perspectives of diverse stakeholders including traditionally disempowered communities, to help shape products responsibly. She currently works on AI across speech and computer vision as well as mixed reality. Prior to joining Microsoft, she worked on creating human-machine systems that enable people to be effective in complex environments in aviation and healthcare. She has published ~40 articles on a range of topics from patient safety, affective computing, and human-robot interaction, and has delivered ~50 talks at national and international conferences. Her book Design for Health: Applications of Human Factors released early this year. Arathi has a PhD in Human Factors Psychology and an undergraduate degree in Computer Science. We talk about why it’s important to have teams like Arathi’s in organizations and how to use data ethnically. We also talk about how her team brings perspectives from other people and communities into their work. Episode Notes Arathi on LinkedIn Responsible AI at Microsoft TED Talk: Carole Cadwalladr, It’s not about privacy–it’s about power Wall Street Journal | Fraudsters Used AI to Mimic CEO’s Voice in Unusual Cybercrime Case Fortune | Amazon Reportedly Killed an AI Recruitment System Because It Couldn’t Stop the Tool from Discriminating Against Women American Psychological Association | The ethics of innovation iTunes Spotify Stitcher TuneIn Google Podcasts PolicyViz Newsletter Related Episodes Episode #68 with Randal Olson Episode #179 with Kandrea Wade Support the Show This show is completely listener-supported. There are no ads on the show notes page or in the audio. If you would like to financially support the show, please check out my Patreon page, where just for a few bucks a month, you can get a sneak peek at guests, grab stickers, or even a podcast mug. Your support helps me cover audio editing services, transcription services, and more. You can also support the show by sharing it with others and reviewing it on iTunes or your favorite podcast provider. Featured image by Gertrūda Valasevičiūtė on Unsplash Patreon Transcript Welcome back to the PolicyViz podcast. I am your host, Jon Schwabish. I hope you were all well and healthy. Last week was a surprise episode of the podcast where I talked to Robert Kosara and Alvitta Ottley about the IEEEVIS annual conference that took place just a few weeks ago. So I hope you had a chance to listen to that. You should also check out the Data Stories podcast that was released a few days later that also cover the IEEEVIS conference. As with any conference like that, it’s not going to be covered in any single or pair of podcasts. So there’s a lot of great information that you should consider checking out over at the IEEEVIS website. So on this week’s episode of the show, we turn back to data, and data visualization, and ethical use of data. And I’m happy to have Arathi Sethumadhavan on the show. She is the head of User Research for Ethics & Society at Microsoft. And I found out about her through a LinkedIn article that was very interesting on the work that she and her team are doing over at Microsoft. Arathi has published over I think 40 articles on a range of topics: patient safety, human robot interaction, affective computing. And she also has a book designed for health applications of human factors that came out early this year. So we talk about in this week’s episode, we talk about her work, talk about our team’s work, and what it means to think about ethics and society as it relates to data, as it relates to, well, products at Microsoft, and as it relates to artificial intelligence, which is clearly going to be and is becoming a major force in all of our lives, especially those of us who are working in data and those of us who are working in the field of data visualization. So I hope you’ll enjoy this week’s episode of the show. And here is my discussion with Arathi. Jon Schwabish: Hi, Arathi. How are you? Thank you for coming on the show and taking time out of your day. Arathi Sethumadhavan: Well, thank you so much. I’m really happy to be here. JS: I am excited to chat with you. I have been reading about you and your work. And it sounds very exciting. And I want to learn more about it, because the ethics around data, product development, communication is certainly important, especially in the moment that we’re having here in the United States. So I thought we would start with have you maybe talk a little bit about yourself and your background, and the team that you’re leading at Microsoft. And then we can get into talking about what the role is, and what the team does, and how you work with all sorts of folks over there at Microsoft. AS: So my background is, my undergrad is in computer science. And I grew up in India, and then I moved to the United States for grad school. And my PhD work is in engineering psychology. So essentially, I study how people interact with complex systems. And most of my work in my grad school days was focused on aviation. And then I moved from aviation to another safety critical industry, post graduation, which is healthcare. And I worked on medical device product development for the longest time. And fast forward to today, I’m on a team at Microsoft, within Microsoft’s cloud and artificial intelligence business, and the team is called Ethics and Society. JS: So, Arathi, when it comes to the work that you do, what do you mean by ethics in terms of the work that you are all doing? AS: Ah, I’m glad you asked that question. Ethics means a few different things to me. Ethics is a responsibility to understand and respect the values, needs, and concerns of end users and other impacted community members, including those who are not our direct paying customer. Ethics is being proactive and not reactive, right? So this means considering potentially harmful consequences of the technology, and mitigating those prior to release. And that actually can result in bigger trust in our brand too. Ethics for me is also translating principles to everyday work. So what I mean to say that it’s very important to have role based training and tools and practices that engineering teams can use to translate these principles into practice. And lastly, I do want to say one thing, I view of epics as innovation, embedding a multidisciplinary team and using multidisciplinary approaches, I think is really, really possible to create exceptional products, services and tools for our customers. So this can actually be a competitive advantage. So, ethics doesn’t have to be viewed as one compliance thing that you have to do. But instead, it can actually be a competitive advantage for you. JS: Yeah. I mean, it’s fascinating. I guess, it shouldn’t really surprise me. But the group itself is not that old. Right? AS: That’s right. The group started in its current state during April of 2018 or so. So we’ve been around little over two years. But manager has been leading a similar sort of a team. It was at that point called Biz AI and Ethics. She had that prior to this particular team being formed. JS: Can you talk a little bit about like, how big is the team or the folks on the team? Are they computer scientists that you have a PhD in engineering? Psychology is a whole other conversation we can have at some point, I’m fascinated. What are the background of the folks on your team? AS: Yeah, that’s a great question. We’re actually quite a multidisciplinary team. And that’s intentionally so because we believe that we can innovate responsibly, if we are able to bring diverse perspectives, right? And so that we are able to challenge these dominant views. So, therefore, our team comprises people like me. So I lead the user research discipline within the team. And my role is really to bring the perspectives of impacted community members into shaping products. We also have designers, and project managers, and engineers. So it’s quite an interdisciplinary team. I would say about 30 or so is our team size at the moment. JS: Wow! Yeah, that’s pretty sizable. So this moment of responsible development of technology, it has, I think, taken shape and hold at several organizations around the world. And I was hoping that you could talk about why this is important, especially right now in the, in the conversation that we’re having around the country. AS: Yeah, I mean, there are obviously really remarkable and well-intentioned applications of technology, right, especially if you think of AI and other emerging technologies. I mean, AI is transforming a lot of major industries that you can think of, from healthcare, to agriculture, to transportation. But there’s a flip side, right? And that is that lot of these technologies are being deployed with very little assessment of the impact that these can have on individuals and societies. I don’t know whether you’ve seen this, John. But last year, there was a news article that came out where a voice deepfake was used to scam a CEO based in UK. JS: Yeah, remember that. AS: Yeah. Then you, you may have seen the article that came out on an AI recruiting tool that automatically categorize male candidates as being superior to female candidates. Of course, you know about the role of social media in terms of misleading vulnerable voters. I mean, there was a great TED Talk by Carole on role of social media on Brexit. You hear news about racial disparities in automatic speech recognition systems. We hear about facial recognition systems, and how that discriminates against certain groups, and so on, right? So, so the point here is that it’s very important to define the current and next generation of technological experiences with intention. And that’s why organizations are starting to pay a lot of attention to it. JS: Because essentially, the argument that you’re making, right, is that these processes and programs are good for the bottom line, which is, I think, something that is an argument that more and more people are starting to make where it’s not just you don’t just do these, because you feel like you need to have more women on the board; you need to do these things, because having more women on the board makes you a better, more profitable company. It’s an argument, I think, that goes a long way. From your role at Microsoft, is it just on the products that are going outside the organization to, you know, what I can buy at the Microsoft Store? Or is it also embedded within the internal work, and also, I would think spreading to the culture of the work inside the organization? AS: Ah, that’s a really interesting question. So, so here’s the thing, right? Of course, it has to manifest in the products that you’re building. But in order to do that, well, you got to have the right processes in place. And you got to acknowledge that it’s people who build these technologies, right? So it’s very important to have the right sort of organizational culture and mindset. And we do that in few different ways. We do that through developing role specific workshops for different disciplines. You know, we try to obtain leadership sponsorship for ethical product development. We also have to do a lot of work in terms of incentivizing ethics and making that a core priority in how people think about products. So that’s, that’s super important. Yeah, the culture is really, really important, because, end of the day, people create these products. Then it’s really bringing the perspectives of diverse individuals into product development, you know, and by that, I mean, talking to impacted groups, and community members, and really using that to challenge dominant views. And it’s really about pursuing principal product development. And luckily for us, we are in this phase where our leaders have created that for us, Microsoft published something called the Future Computer. It was a publication of Microsoft, where our leaders Harry Shum, Brad Smith talks about six ethical principles for AI, which is fairness and inclusion, transparency, privacy and security, reliability, and safety, and accountability. And we apply these principles when creating products. So these are all the things that you need to do within the organization. But you also have to realize that we are in an environment where things are constantly shifting, things are constantly changing; new regulations are emerging, you know, and national events, international events, all of these can even instill feelings of trust, could instill feelings of fear amongst people or end users, right, towards technologies. So it’s very important to take these into account and respond to new knowledge as it emerges as well. JS: Mm hmm. I’m curious, as you already mentioned, you have a pretty heterogeneous group of people in the group itself. And I’m curious, you mentioned also that you talk to stakeholders and members of the community. And I’m curious, for the more quantitative people on the team, is that hard to do? This is something that I’ve been curious about talking about with people lately that, you know, people who are trained and tend to do quantitative methods and quantitative work, this idea of talking to actual people, talking to people that we study, and people that we communicate with, is a pretty foreign idea in terms of, you know, we don’t do that. We download data, we collect data, and we analyze it, but we don’t actually talk to people. So like, do you find that it’s hard for some people to do that? And do you find that by having this broad team with all these different skill sets, you’re able to, in some sense, kind of train people on how to be good at having these, these stakeholder meetings and outreach efforts? AS: Yeah, yeah. So luckily, for us, we are a large company, right? JS: Yeah. Yeah. AS: So that sort of, I’d say the privilege of having different disciplines. So we don’t expect, you know, an engineer or a data scientist necessarily to go and engage directly with stakeholders. We do expect them to think through some of these questions and the benefits and harms that the technologies that they’re working on can have on these human beings, but we don’t necessarily expect them to do the direct engagement with the community, because that might not be their area of expertise. It’s a totally different skill set. So at Microsoft, we have human centered disciplines, like user researchers and designers. So this kind of owners, we put on those disciplines. So now for within the Ethics and Society team, I lead the user research discipline. And what my team does is just that, which is engage with the community. And we do that through a variety of qualitative and quantitative research techniques. JS: So when it comes to this responsible product development, you bring together these, these different perspectives. So how does that ultimately inform? You talked about this a little bit, but I’m just curious how you take those perspectives and inform them the final product? And to expand on that a little bit, how do you convince the developers at other places at Microsoft, that these are components that they should bring into their work? I’ll give you a good example from my experience, which is, which is obviously very different. But you know, very early on in my tenure at Urban, I was trying to reduce the number of pie charts that were people recreating at my organization. So people are making pie charts with 12 slices in them. And we, you know, that’s not a good technique. But whenever I would try to argue that someone should not use that, they wanted evidence to support my argument. They’re researchers, so they want this, this sort of hard evidence to support my argument. So when it comes to the responsible product development, how do you bring the different perspectives of people that you’ve talked to in your team and convince your other colleagues at Microsoft to embody and embrace these, these concepts and ideas? AS: Yeah, so I think the answer sort of lies in your question itself. I think there’s a lot of power in the actual perspectives of these different stakeholders. So like I said earlier, we use a lot of qualitative and quantitative research techniques to, to solicit the feedback from end users and other community members. And what we do is we conduct our interviews or large public perception surveys. We do community [00:15:23 inaudible] where we assemble and line up product teams with the impacted community members so that they can hear the perspectives of these individuals, you know, directly. This, these quotes are extremely powerful. JS: So what do you mean by bringing perspectives in from other people into the work that you all do? AS: Through a few ways we do this. One is as part of your product development process, ensure that you have considered a diverse pool of end users, most importantly, including end users who are typically forgotten or excluded. So this means that you’re intentionally going out and recruiting people from the LGBTQ plus community, racial minority groups, women, introverts, those with visual impairment, speech impairment, and so on. We believe that when we can address the needs and concerns of marginalized communities, we are able to better address the needs of a broader range of people. The second point I will make here is think about your indirect stakeholders, in addition to your end users and other direct stakeholders. So these could be individuals whose jobs could be impacted by the technology you’re building, for example. Third point here is to seek advice from domain experts and human rights groups, especially as you work in novel complex domains. We worked with experts on situation awareness, tech policy law, human rights on different projects, as we realize our expertise in some of these spaces may be limited, and these individuals can actually help us. When product teams hear firsthand the needs, and the values, and concerns of the community directly, then that’s really powerful. So that helps a lot. And the important thing to realize is that getting feedback is not like a onetime thing that you do. And then you call it a day. I’m talking about getting feedback from the community throughout your product development lifecycle. You know, if you think about it right from envisioning to defining the problem space, to prototyping and building to post-deployment, throughout all of these phases, you got to engage the community and learn. I mean, that’s the only way, you know, you can create a superior product. Now, you asked about convincing. So the data speaks for itself. So that helps immensely. And two, I like to think that most people have good intentions in mind. So when they see data, it’s easy to persuade them. Three, I have to say that when we engage with different product teams, we have a formal handshake that happens with the leadership of that product team. People create products, and unless of the ways you change the organizational sort of mindset, it’s very difficult to do anything anywhere. We work very closely with product teams. We have this like strong interest, strong buy in on the kind of work that we are bringing to the table. So that immensely helps as well. JS: Yeah, yeah. No, it’s, it’s interesting, because it seems like not only does it affect the product, it affects the culture of the, of the people that you work with. And then that continues through the lifespan of the product and into the next product or what have you. AS: I do have to say, John, that when the teams that we partner with, when they see the rigor and the processes that we bring to the table, you know, we do end to end engagement with them. And that includes doing harms modeling sort of exercises to anticipate what can go wrong with the technology and what could be the impact to different stakeholders, from that to the research with the community, right? It could be qualitative research activities, or more quantitative sessions. JS: I’m trying to get my head around some of this. Do you have an example of a product that could harm a customer or a stakeholder and, and then how your team would come in and say, with evidence and say, this is how we might go about address, I don’t, I don’t think it sounds like for what we’ve talked about already, doesn’t sound like you come in with a fix. You come in with suggestions and data, but I’m just curious if can you give us like a concrete example so I can get my head around, like, what would a harmful product look like? AS: Certainly. So there was a product that we worked on, called Custom Your Voice. So the whole idea here is you can take snippets of someone’s voice. So you just need 500 to 1,000 utterances of somebody’s voice. And this can result in a voice pond. But the thing about that is, it could then say things that you never uttered, actually to like a voice deepfake, right? So if you think about it, the huge repercussions if this is not developed, right. So we did a lot of very interesting work in this front. We created a gating process around this particular technology so that it’s not available to everyone freely in the market. We vet the enterprise customers that we would be providing the service to. We worked with voice actors, because they are a group of individuals whose jobs could be impacted by this particular technology to understand their perspectives. And this resulted in a set of guidelines around how companies that use the service need to be transparent with this group of individuals. And so that became a part of our terms and conditions. And this service actually has a lot of benefits too, right. If you think about it, it can be hugely advantageous for people who don’t have a voice, who have speech impediments, like it can be a confidence booster. So we also did a lot of primary research with individuals with speech impairments to try to understand their unique needs. And that resulted in a set of guidelines around how to create this service that caters to the needs of this group of individuals. And lastly, I have to say that it’s very important when humans interact with different experiences that they don’t feel deceived. And it’s very easy in a situation like this, where you’re interacting with a synthetic voice, because this can be extremely realistic sounding. So it’s very easy to feel deceived if you don’t know that you’re actually interacting with an agent, you know, automated agent. So we also worked. In fact, my team, we did a bunch of studies, to understand the right disclosure that’s needed for consumers when interacting with synthetic voice. So we approach mitigations in like different angles. JS: Right. So it’s not necessarily making the voice sound more computerized necessarily to get away from that problem. But it can be about warnings on the product, so that, so that the consumer is aware of it. AS: That’s right, because extremely low fidelity kind of voice can actually be really disturbing. That hampers the user experience. Do you keep it high fidelity, but at the same time, make sure that it’s an authentic experience, and there is no deception that’s happening? I want to add, though, John, that there are absolutely certain situations that we recommend a high fidelity synthetic voice not be used, you know. JS: Oh, okay, yeah. AS: Be appropriate in all scenarios, like, for instance, if you’re calling 911. And that takes you to a high fidelity, human sounding voice. Even if there’s some sort of disclosure, it can give you a false sense of, you know, confidence on the consumer, because you tend to equate a high fidelity sounding voice to having high level of capabilities. And that may not be the case all the time, because it’s still an artificial agent. So there are absolutely certain situations where you want to avoid using that fidelity. And we outline all of those in our, in our guidelines for responsible development of this tech. JS: Right. That’s really interesting. Okay. So I want to close up by maybe taking a practical, concrete approach to this. So are there specific tools, techniques, actions that you would recommend for responsible and ethical development of technology? And I might even throw in use of data as well. I think, you know, probably a lot of people listening to this, this podcast are working with data day in and day out. They may not be creating physical products, but they’re working with data. And so I’m just curious about, you know, what sort of techniques and tools you and your team would recommend for, for those folks? AS: Yes. So, I suggest that technologies actually do the following. And I’m going to talk about, like ten things. Okay. So the first one is really simple. It’s pretty basic if you think about it. It’s really trying to determine what problem are you trying to solve? Is there actually a technology need for this problem? We actually discussed this a lot. Is this a human problem? Or is this a problem that can actually be solved through technology? So you want to understand that first. Two, who are your impacted stakeholders? And by that I mean end users as well as other stakeholders who can be indirectly impacted by the technology. For example, people whose jobs could be impacted by the technologies that you’re building. Third step is really thinking through what are the benefits of this technology for each of the stakeholders that you just identified? And then what could be the potential harms? I suggest using some sort of analytical approach to systematically think through these benefits and harms. Internally, we have developed certain tools that help us do this in a systematic manner. But there are also frameworks available in the public such as value sensitive design frameworks that can help think through what are the values, and concerns, and beliefs of different stakeholders? And what could be the potential harm that these technologies can bring? Then I would really suggest that you think through some of the key ethical principles such as fairness, reliability, privacy and security, inclusion, transparency, all of that. So by that, I mean, asking yourself some key questions, which is, does your system treat all stakeholders equitably and prevent undesirable stereotypes? Does the system run safely even in the worst case scenario? Is the data protected from misuse and unintentional access? Has the system being created in an inclusive manner to make sure that there are no barriers that could unintentionally exclude certain groups of people? Are the outputs of the system are understandable to the end users? And are you finally taking accountability for how the systems are operating and scaling and its impact on society? Sixth point that I would say is really including diverse disciplines as part of your product development process that includes social scientists, human rights groups, designers. And that’s really important, like I said earlier, to challenge dominant perspectives. Point seven is really need to make sure that once you’ve identified these harms, create the right sort of work streams to mitigate these harms, which includes involving diverse stakeholders throughout all stages of product development from envisioning to post deployment. Then eight point is, like I said earlier, acknowledging that people develop technologies. So you’ve got to create structures where people are actually incentivized for making ethics a core priority of work. Point nine is making sure that you have developed role-based training, best practices, and tools that product teams can use, because principles will only go a certain way. So unless you have tools and best practices that teams can adopt and run with, you’re not going to be successful. And of course, you also need processes that will hold the product teams accountable. So those are sort of my 10 points. But I want to close with saying that it’s really important to recognize your domains of ignorance, right? Because this is a new space. For all of us, this is a new space. We are learning by doing. And so having that humility is very, very important. JS: Yeah, I think that’s a great point to end on. And I would probably add empathy to that. But the humility of saying that you don’t know and you’re willing to have these conversations and make these tough choices is such a key part of everything that you’re doing. Well, Arathi, thank you so much for coming on the show. This is fascinating stuff. I hope others will learn from your experience at Microsoft and be able to hopefully take some of these tips. We’ve got 10 tips, which is great. Take these into account in their own work. AS: Well, thank you so much. JS: Thanks, everyone, for tuning into this week’s episode of the podcast. I hope you enjoyed it. I hope you will check out Arathi’s work and the work of her team over at Microsoft. And I hope you will consider supporting the podcast. Please tell your friends and colleagues about it. Write a review on iTunes or wherever you listen to this podcast or head over to my Patreon page, where for just a few bucks a month, you can help support the podcast, the transcription, the web servicing, the audio editing, all that good stuff that allow me to bring the show to you. All right. Well, until next time, this has been the PolicyViz Podcast. Thanks so much for listening. A number of people help bring you the PolicyViz Podcast. Music is provided by the NRIS. Audio editing is provided by Ken Skaggs. And each episode is transcribed by Jenny Transcription Services. If you’d like to help support the podcast, please visit our Patreon page at patreon.com/policyviz. The post Episode #185: Arathi Sethumadhavan appeared first on Policy Viz.
47 minutes | 19 days ago
Episode #184: IEEEVIS Recap
The annual IEEE Visualization Conference is an annual conference in scientific visualization, information visualization, and visual analytics. I’ve attended only a few times in the past, but with this year’s conference being virtual, it was an easier opportunity to present papers, learn about the state of data visualization research, and say hi to folks I haven’t seen in a while. When the conference concluded, I reached out to Robert Kosara from Tableau and Alvitta Ottley from Washington University to come chat about what they saw, what they learned, and what they thought about the virtual set up. Dr. Alvitta Ottley is an Assistant Professor in the Department of Computer Science & Engineering at Washington University in St. Louis, Missouri, USA. She is also the director of the Visual Data Analysis Group. Her research uses interdisciplinary approaches to solve problems such as how best to display information for effective decision-making and how to design human-in-the-loop visual analytics interfaces that are more attuned to the way people think. Dr. Ottley was the recipient of an NSF CRII Award in 2018 for using visualization to support medical decision-making. Her work has appeared in leading conferences and journals such as CHI, VIS, and TVCG. Robert Kosara is research scientist at Tableau and has been part of the visualization community for more than 20 years. His research interests include data presentation and communication, as well as unusual or under-researched visualization techniques like connected scatterplots, ISOTYPE, and pie charts. He runs the eagereyes visualization blog and associated Youtube channel. This is a special episode of the podcast! I’m breaking my every-other-week regular schedule to bring you this interview as close to the conference as I could get it! I hope you enjoy and can check out some of the papers we discussed. Episode Notes Robert Kosara (EagerEyes) | Twitter | YouTube Alvitta Ottley | Twitter | Visual Data Analysis Group at Washington University IEEEVIS Main Conference Website VisComm Workshop VisActivities Workshop BELIV Workshop Papers -Jon Schwabish and Alice Feng, Applying Racial Equity Awareness in Data Visualization (summary blog post) -Melanie Bancilhon, Zhengliang Liu, and Alvitta Ottley. Let’s Gamble: How a Poor Visualization Can Elicit Risky Behavior. -Sunwoo Ha, Adam Kern, Melanie Bancilhon, and Alvitta Ottley. Expectation Versus Reality: The Failed Evaluation of a Mixed-Initiative Visualization System. -Shayan Monadjemi, Roman Garnett, and Alvitta Ottley. Competing Models: Inferring Exploration Patterns and Information Relevance via Bayesian Model Selection. -InfoVis Best Paper: Alex Kale, Matthew Kay, Jessica Hullman, Visual Reasoning Strategies for Effect Size Judgments and Decisions -Melanie Bancilhon, Zhengliang Liu, and Alvitta Ottley, Let’s Gamble: How a Poor Visualization Can Elicit Risky Behavior -Cristina Ceja, Caitlyn McColeman, Cindy Xiong, Steven Franconeri, Truth or Square: Aspect Ratio Biases Recall of Position Encodings iTunes Spotify Stitcher TuneIn Google Podcasts PolicyViz Newsletter Related Episodes Episode #161: Drew Skau and Robert Kosara Support the Show This show is completely listener-supported. There are no ads on the show notes page or in the audio. If you would like to financially support the show, please check out my Patreon page, where just for a few bucks a month, you can get a sneak peek at guests, grab stickers, or even a podcast mug. Your support helps me cover audio editing services, transcription services, and more. You can also support the show by sharing it with others and reviewing it on iTunes or your favorite podcast provider. Patreon The post Episode #184: IEEEVIS Recap appeared first on Policy Viz.
37 minutes | a month ago
Episode #183: Safiya Noble
Dr. Safiya Umoja Noble is an Associate Professor at the University of California, Los Angeles (UCLA) in the Department of Information Studies where she serves as the Co-Founder and Co-Director of the UCLA Center for Critical Internet Inquiry (C2i2). She also holds appointments in African American Studies and Gender Studies. She is a Research Associate at the Oxford Internet Institute at the University of Oxford and has been appointed as a Commissioner on the Oxford Commission on AI & Good Governance (OxCAIGG). She is a board member of the Cyber Civil Rights Initiative, serving those vulnerable to online harassment. Previously, she was a visiting faculty member to the USC Annenberg School for Communication and Journalism, and began her academic career as an Assistant Professor in the College of Media and the Institute of Communications Research at the University of Illinois at Urbana-Champaign. In this week’s episode, we talk about her work, her book, and the intersection of race and technology. We also talk about the concept of “technological redlining” and what this kind of discrimination means for people of color today and in the future. Episode Notes Safiya’s book, Algorithms of Oppression: How Search Engines Reinforce Racism Safiya’s website Safiya on Twitter UCLA Center for Critical Internet Inquiry UCLA Center for Critical Internet Inquiry on Twitter Hypervisible Exchanges blog from Chris Gilliard Other Books –Stamped from the Beginning, Ibram Kendi –Color of Law, Richard Rothstein –Lower Ed: The Troubling Rise of For-Profit Colleges in the New Economy, Tressie McMillan Cottom iTunes Spotify Stitcher TuneIn Google Podcasts PolicyViz Newsletter Related Episodes Episode #181 with Virginia Eubanks Episode #179 with Kandrea Wade Support the Show This show is completely listener-supported. There are no ads on the show notes page or in the audio. If you would like to financially support the show, please check out my Patreon page, where just for a few bucks a month, you can get a sneak peek at guests, grab stickers, or even a podcast mug. Your support helps me cover audio editing services, transcription services, and more. You can also support the show by sharing it with others and reviewing it on iTunes or your favorite podcast provider. Patreon Transcript Welcome back to the PolicyViz podcast. I’m your host, Jon Schwabish. I hope you and your families are all well and healthy in the strange times. And obviously today here in the United States, it’s election day. So it’s a pretty big day. I remember just a few years ago when people would argue that there wasn’t a big difference between either the major presidential candidates or the political parties. That has certainly changed over the last several years; the differences between the candidates couldn’t be larger, the differences between the parties couldn’t be larger, both in terms of policy and politics in the approach to governing. My hope today is that everyone who wants to vote can vote and does vote, voter suppression strikes at the very core of any democratic country and for any party, any person, any organization to threaten voters is, to me, quite upsetting and count outrageous. So I hope today we have a smooth day in our elections. But okay, enough about politics. And before we get to the show a few things about what’s going on at PolicyViz. First, I’ve restarted my newsletter. Up until this fall, I sent maybe one or two newsletters out each year. It’s a lot of work to do. I didn’t think folks really wanted to hear from me even more than they already do on the blog and the podcast, a plus the way I was writing them, they just got kind of like big advertisements. But I have finally found I think what is a good balance. I have the newsletter going out now every other week. It comes out the day before the podcast. And it talks a little bit about the podcast that people see the next day. So it’s a little bit of a sneak preview. I also write behind the scenes post. It’s kind of a short blog post. I’ve only done two or three already. But it’s pretty much a short blog post. I’ve already written about the last phases of my book writing process for the book that comes out in January. And I’ve written about some database hacking I’ve been doing in Excel. So if you’d like to sign up for the newsletter, the new newsletter, do head over to PolicyViz and check it out. I’ve also added a few more things to the PolicyViz shop; a couple of more shirts. I’ve updated the match at data visualization game, which is now in stock. And if you’re into the R programming language, you’ll find a couple of shirts that I think will strike your fancy just to say. Okay, enough of all that and on to the show. Okay, let me just say, I love all my guests. My guests are great. They do great work. They take time out of their schedules to come sit and chat with me. And I love all of them. I will say, however, that this week’s guest is special. It was a great conversation, super fascinating and I was really excited to have this guest on the show with me. So this week Safiya Noble joins me on the show. Safiya is an associate professor at UCLA, where she serves as the co- founder and the co-director of the Center for Critical Internet Inquiry. She is also the author of the best selling book Algorithms of Oppression. I picked it up over the summer, and I could not put it down and I’m just so happy that she took time out of her schedule to come chat with me on the show. Her book covers racist and sexist algorithmic bias and commercial search engines. It’s a problem that has not yet gone away, as you’ll hear about in the interview. So it was just a pleasure to chat with Safiya Noble. I had a really great time during this interview, as you will certainly hear in the audio. Her work is so interesting. Her perspectives on the intersection of race and technology are so interesting. They’re so important, and they’re invaluable for anyone who works and communicates data. So I hope you will enjoy this week’s episode as much as I did with the interview and putting it together. So here’s my conversation with Safiya Noble. Jon Schwabish: Hi, Safiya! Thanks for coming on the show. It’s great to chat with you. Safiya Noble: It’s great to be here. Thank you. JS: I’m really excited to talk about your book that links are on the show notes page, Algorithms of Oppression: How Search Engines Reinforce Racism. And I want to talk both if we can about the book, and also your current work and what you’re looking at now and the activities and things that you’re looking at, especially in the current era in which we’re in, the moment that we’re in. So, but maybe we can start by having you talked a little bit about yourself and your background, and then you can start talking about the book and where it came from and how you went through the process of writing it and what you’re trying to accomplish with it. SN: Sure. So, , this book was really the outgrowth of my dissertation when I went to grad school and I always like to tell people that I spent my first career in advertising and marketing for about 15 years, and everything that probably isn’t good for us like [Inaudible] [00:05:01] and booze. I was a part of selling all those things. And no, I think of going back to grad school, it’s kind of like, for me. Yeah. When I went to grad school at the University of Illinois at Urbana Champaign, I was thinking about advertising and marketing. That’s the thing that I really knew super well inside and out. I’ve been an expert in multicultural and urban marketing and public relations. And when I went back to grad school, it was kind of a time when, of course, Google was on the rise, Facebook was on the rise. These platforms were becoming platforms before our very eyes. And people were kind of enamored in a way that I thought was kind of curious in academia, because when I was in industry, I was really clear that platforms like Google and Yahoo, in particular, were advertising platforms. I mean, they were places where we were doing pretty significant media buying. We were definitely working with public relations teams to create what we call like advertorial right to make it to the front page of search and to make our clients products and services seem like they were credibly third party verified so to speak. And yet, in academia, people were starting to talk about Google, like it was the new public library. And this disconnect, really was fascinating to me. And that was the impetus for starting to think about well, wait a second, let’s double click a little bit here on Google, in particular, really, as a case, I mean, it could have been Microsoft or Yahoo, but no one really uses them the way they use [Inaudible] [00:06:50]. JS: Right. SN: Fortunately, Google got the attention because they were the monopoly leader. But that’s really how I kind of got to it. And I was because I’d worked in the multicultural and urban marketing space for so long, I was attuned to thinking about how vulnerable communities, marginalized communities, under represented communities, and even trend setting and trend leading communities which I think are of African Americans, for example, my community as being both often politically, socially and economically marginalized, and at the same time being the trendsetters and the trend leaders in popular culture, for example. So it’s like this, interesting duality plurality. That’s where I kind of started looking at how communities were represented in search engines and that really opened up I think, the Pandora’s box that became Algorithms of Oppression. JS: Right. Do you find it odd even today how many people seem to think that these services that we get for free are just free, and there is no other component? I love that word advertorial. SN: Yeah. I mean, it’s still surprises me. On one hand, it surprises me that people who I think should know don’t know. And that kind of that sliver in particular. So I remember recently giving before we were all locked down, I was giving a talk at a big conference and a public librarian approached me. And she said, , I always thought Google was a nonprofit. And I was really [Inaudible] [00:08:30]. And I thought, Oh, my gosh, it was so important to learn that from her, and it made me realize that even the people that we think of as being highly trained and astute, they are also just as susceptible to the kind of marketing discourses that come out of Silicon Valley, as anybody else right. So Silicon Valley has really, for 30 years, tried to convince us that their products are just tools, and that anything that happens in them, is the fault of the public, not the fault of the faulty tool. So we have a lot of work to do for sure, with all kinds of audiences, but I like to focus on data scientists and computer scientists and librarians and teachers and professors and people who I think have kind of like an exponential type of impact on helping us understand what we’re really dealing with. JS: Right. Do you want to talk about an example or two from the book on how the algorithms particularly in the search engines in Google have or still do I guess, misrepresent people of color, underrepresented groups? Is it just to give people if they haven’t registered a flavor of what? And I’ll give people a warning like it’s the examples you show are pretty shocking of how these algorithms work and misrepresent different groups. SN: Yeah, this is probably the point where we should warn everybody that if you are listening to this podcast in the car with the kids, JS: Yeah, this is where you want to turn the volume down,. SN: Pause it and then pick it up with a car [Inaudible] [00:10:12] JS: Yeah. SN: Okay. So what got me going in this study, like the first real study was looking at a variety of keywords that represent women and girls and I, and not just women and girls. I mean, I looked at a lot of different keyword combinations, because the first, let’s say, well crafted study that I did was I took all of the identities represented in the U.S. Census. So I took the gender categories and the racial and ethnicity categories, and I paired them and combo them in 80 different ways. And what was shocking to me and by shocking, I mean, not really shocking, but still shocking, was to seeing how when you paired any kind of ethnic marker, except for white, with the word girls, so African-American, black-black, in particular, and I thought black was important because most , black girls identify as black girls, right? I mean, African-American is kind of a way we might characterize ourselves in more formal settings, but in our families and with our friends many of us just identify as black as kind of a cultural and political identity. And so it was shocking to see that for black girls, Latina girls, Asian girls, the whole first page of search was almost exclusively pornography or hyper sexualized content. And the first of these being I think in 2009, when I was looking at this, the first hit for black girls was hotblackpussy.com and then by 2011, it was sugaryblackpussy.com and you ask yourself, oh, my God, how can this be when you don’t have to add the word sex, or porn, but black have become synonymous, Asian girls, Latina girls just become synonymous without adding any sexualized keywords. And this really is what opened up what became a much longer inquiry into all of the ways in which these systems really fail vulnerable people and that we have a lot of mythology around why this happens. So for years, I would talk about my work, and people would say, well, that’s just because that’s what’s most popular. And I would say, well, how can we say that this is what’s most popular without adding these kind of hyper sexualized keywords and how fair is that? I mean, this is tyranny of the majority that has girls of color in the United States will never be in the majority in a way to affect the way they are misrepresented in search engines. But more importantly they will also in the near term future, as children never have the capital to be able to use AdWords and keyword planning tools and all of these, the back end, that helps optimization happen. And of course, the porn industry is masterful at many-many technologies that we have are really due to the research and investment that they’ve made. But also, is it fair? And is it moral? And is it ethical? And that’s really what is the kind of opening salvo to the book, Algorithms of Oppression, which follows with many-many more kind of gruesome tales of misrepresentation arm. JS: You know, it’s really interesting, I read Ibram Kendi’s book Stamped From The Beginning after reading your book, and the entire I mean, at least the first half of the book, he talks about how the way that white people would describe the black community and people in bondage was hyper sexualizing, both men and women. And so for someone to say that what happens in the algorithms or what happens in their Google search, is because that’s what’s popular, is sort of ignoring a long history of the way the white power structure has viewed, and I guess pushed down the black community in this country. SN: It’s really true. I mean, I try to give a whole historical and kind of sociological context like Ibram’s book does. I mean, his book came out around the same time. I wasn’t able to read it before. And while I was writing my book, I wish I could have I mean, it’s, but these histories many black feminists have written for decades about the gruesome, hyper sexualization of African peoples and of course, one of the reasons for that is because it helps justify the enslavement of people, right? The degradation is part of the dehumanization of people. And when you dehumanize people, you are much less attuned to their loss of rights, the ways in which they are discriminated against, the way in which they are oppressed. You lose empathy and the ability to care. And so talking about how to humanization happens in the digital space is very important, because it is also a many of the ways that we’re engaging online are dehumanizing. And they’re racialized and gendered in that dehumanization. And if we think there isn’t a relationship between people coming across disinformation, propaganda, racist ideologies, that seems so subtle, right? I mean, I can’t tell you how many thousands of people have told me over the last decade, that what shows up on the first page of search is just what it is. And there is no politics, right? There is no power. there is nothing nefarious happening. It’s just what it is. I actually had a professor once say to me, when I was a grad student in presenting my work, he said, maybe black girls just do more porn. I mean, the rarity, right of the way in which people would justify these kinds of problems, is really why I mean, I try to expose people in this book to how to think about the way in which we’ve all been socialized in racist systems that then make it very difficult when we are designers and makers of technology also to even be willing to look at these problems. JS: Right. I want to pivot a little bit and sort of look, because we’ve looked back a little bit, I want to look forward. So in this book, and also elsewhere, and some of your writings, you’ve sort of coined this term, technological redlining, which I think is really a fascinating concept. And I’ll just for a brief moment, talk about what redlining is, for listeners who don’t know. So in the early part of the 20th century, there was a federal government agency whose job it was to assess housing values in major metropolitan areas around the country. They created these maps where that assessed risk and the red areas of those maps are the highest or the highest risk areas, but those red areas could have just one black household living in that area. And those will be assessed the highest risk and those the impact of those maps has persisted for generations to impact the accumulation of wealth, accumulation of income and neighborhood segregation in the U.S. And so, Safiya I’m interested, from your perspective, when you think about technological redlining how does that affect groups now? And then also in the future does it have the same intergenerational effect going forward? SN: It’s such a great question. You know, as I was writing the book, I didn’t know about the genius of my colleague, Chris Gilliard [ph], who also writes about digital redlining. And I think of our concepts of kind of technological or digital redlining as being quite similar in that. What we’re arguing is that these systems, all kinds of digital engagements, not just search. I mean, every type of digital engagement is really tied to tracking, surveilling, categorizing, and predicting us into certain types of futures. And those futures often and the way in which we’re classified digitally, is usually something we have no ability to affect for the most part, but we really don’t know what our digital profiles are. We don’t know, just like, African-Americans in the past, did not know that they lived in zip codes that were being redlined and keeping them out of mortgages, right, and financial services. In many ways, those same processes are happening now. Our digital traces, information gathered about us is also used when we are doing things like looking for insurance quotes online or looking for different kinds of banking and educational products. There is a great book called Lower Ed, by Tressie McMillan Cottom, where she talks about how over and over again poor women and especially poor women of color, black women, whose digital traces are embedded in these systems that they’re using all the time. When they go to look for things like higher education, they are targeted with predatory ads, that push like the Trump universities that the like for profit predatory kinds of educational, I use acquitting fingers right; scams, quite frankly, and are targeted for all kinds of predatory products. So these are the kinds of ways that we’re talking about digital or technological redlining. The thing that I worry about the most that I’m studying are really are the predictive analytics that underlies so many of the systems we’re engaging with because I don’t think that it’s far fetched to see how systems of social credit, which really originate in the United States, with our own kind of credit scoring systems, certainly are in play in the UK. We put a lot of focus on China. But we want to remember, a lot of these models started, before China picked them up. So these social credit, these predicting models about who has access to all kinds of opportunities, goods and services in the future, really are the fundamental logics of so many products now. And I think these are things that are hidden from view. People don’t understand them. When they hit the news, like algorithms predicting who would get to go to college or not in the UK, which hit about a month ago, then people kind of realize, like, whoa, whoa, wait, what, but until people start to understand that many of the future opportunities for our kids and our next generations are going to be over determined by algorithms and AI then we haven’t done enough work yet. And I think we need regulation, we need policy and we need a huge amount of social awareness about what these technologies, I think, are going to do to our quality of life and the way we live. JS: Yeah. So on that note, so what can people do to combat the algorithmic racism, and especially for people who might be listening to podcasts who are sort of immersed in data, immersed in technology, what can we do if anything to address some of these inequities? SN: It’s really a good question. I mean, I think there is intervention on a couple of levels. Well, first, let me say, for data scientists and people who work with data, one of the things I always try to teach my students at UCLA is that data, like race and gender are social constructs. Data come from somewhere, and they are incredibly subjective. People think that data is somehow neutral, that it just is what it is, that it has no politics, but see, those of us who make data, people who are researchers, we know, for example, the sociologist news that where they kind of mark the beginning of one category and the end of the next category it’s sometimes arbitrary, it’s sometimes a guesstimate, it’s sometimes kind of like the best you can do. It’s sometimes just a number. It’s just 25% and that’s what it is. And, and that kind of data making is also often especially we’re talking about big data sets, it’s representative data. It isn’t a close reading of people representative data is, it’s somebody’s grandma, it’s it’s simply his family member. It’s people who are vulnerable, who can’t speak for themselves who can’t clarify and nuance the way in which they’re represented. So this kind of representative data gets used and taken up like a truth teller. And that is very dangerous. So I think we got to pull back and think about what we’re doing when we’re doing things with data. There are people who obviously, there are so many tech workers who are also intervening at the level of just saying, I’m not going to work on certain products, because I think these are unethical products, or these are going to change the future in a way that we don’t want. We only wish for example, you know, I look at people like climate scientists now, who lament the fact that all they did was kind of put the information out and say, hey, the way human beings are living in an organized society is really going to kill the planet. Here is the research, but they didn’t take an activist stance about that work like there. JS: Yeah. SN: I think we have to those of us who work closest to these models, we have to take a more activist stance about the harm and say, wait a minute, we don’t want to do this, this is dangerous, and not lay back, like the climate scientists until it’s too late. And then I think for everyday people, obviously, there is so many ways that we should be thinking about protecting our data, but these things in our privacy and like how we engage with the internet, but I think we need to really exercise our power by getting candidates educated. We need to vote on policymakers that are astute about these issues. Many of the interventions can happen at local and state levels. We certainly see like ban on facial recognition, greater privacy laws in California where I live; those things don’t have to all just go to the federal government. And I just want to remind people that we still have a lot of power to connect and organize locally where we live. JS: Yeah. I wanted to ask, and you may not know the answer to this, but are there states or countries or are there jurisdictions that are or even individual policymakers that are leading in these efforts to address these issues? SN: Well, I think Senator Warner’s office, the Senator from Virginia has been way out front on things like antitrust and harm. That’s coming from big tech, let’s say broadly as a category. And I think we want to support and watch what’s happening in his office. I think that on the Federal Trade Commission, we have Commissioner Rohit Chopra, who is one of the best and smartest thinkers about consumer harm. So he, for example, is taking on the cases where automotive dealerships, for example, not only tell their sales people that when you see black and Latino customers approach to mark the price of the car up no matter what, but also, that are using predatory software that gives higher interest rates to African-American and Latino customers. He has taken them on, and took on a case out of New York. And so I think that there are individual policymakers who are definitely trying to create a critical mass on the federal level. I think California has been a place where we can test a lot more thinking and it’s a place where we need to. I mean we are, here we are the heart of Silicon Valley is right in Silicon beach, right down the street from where I live. And here you have, like the largest industry in the world, doesn’t pay taxes, doesn’t pay back into the system, takes the cream of the crop, students into their employee ranks, and then really gives back so little to society. So I think there are people, certainly those of us in the UC system at UCLA, our Center for Critical Internet Inquiry, we’re really trying to track these things and your subscribers can certainly follow us. And we can try to keep pushing out resources. I think in Europe, the EU has been the most probably aggressive; Germany and France, around thinking about the harms of data. But it’s very complicated, because the scale of these systems is so intense that when you talk about taking on these systems, you’re talking about taking on the entire financial services sector, which is all digital now. You’re talking about taking on the markets, the global financial markets. You’re talking about taking on state governments that are now so deeply intertwined into digital systems that it’s quite difficult to see where big tech companies begin and end and states begin and end. And then of course, you have companies themselves, like Facebook, who are operating at the level of nation state unto themselves trying to make their own currency, trying to make their own laws to govern themselves. So there are many points of pressure that I think we need to be paying attention to. JS: Right. I can’t decide which final question I want to ask so I am going to ask both of them. This is so fascinating. Let me ask this one first. So do you think now we’re in October of 2020. So it’s a it’s an odd time for many reasons. But I want to ask whether you think things are changing for the better or changing for the worse? And I can’t really tell based on what you said so far, whether you think we’re going uphill or downhill on this? SN: Well, I think that we haven’t bottomed out yet. And so that makes me nervous because they think one of the things that I say in the book is that we have more data and technology than ever. JS: Yeah. SN: We’ll have more global social kind of political and economic inequality to go with it. So one of the things we know, for example, is that the promises of the internet and the promises of digital technologies was that they were going to even out the world, right, make things more equitable, make democracy more plausible, accessible. What we’ve seen, though, is that many of these technologies are being used in service of the kind of rise of authoritarian regimes, including in the United States, I’m sorry to say. We’re seeing again, a level of control and lack of transparency by so many of these actors between the tech sector and governments that I’m not actually feeling hopeful yet like that everything will turn on the next election. What I will say, though, is that we are in the era of kind of the tech lash and that people are starting to think more critically. More films and television shows, and scholars and thinkers and podcasters and public intellectuals, like yourself, are taking up these issues in a way that they really did not 10 years ago. So in that way I’m super energized, that we’re building a critical mass of people. And you know, I like to think of it this way that right now I’m writing about the relationship between three different eras; the era of big cotton, the era of big tobacco and the era of big tech. And one of the things that they all have in common is that it really was a small group of people who were kind of abolitionists, who took on huge industries that people thought could never be taken on. And they shifted our, the paradigm of how we think about the enslavement of African peoples. They shifted the paradigm of how we think about the public health crisis associated with big tobacco is just what it is, and there is nothing we can do about it. And I think we’re in a moment where some of us are thinking about what technologies should be abolished? What should be made illegal? What is too harmful? What are the secondary, tertiary effects of some of these technologies? Even if you aren’t on Facebook, how you’re affected by those kinds of platforms? So I think that in that way, if we pull back and take a long view, instead of a short view, I feel hopeful that there will be enough of us who will talk about the ethical and moral feelings of the institution of big tech, and shift is towards something better. And I believe we’ve already done that. Human beings have already done that in different moments. I think we’ll do it again around this moment. JS: Well, like the way you shifted from the pessimistic outlook to the optimistic outlook. So that’s.. SN: That’s the best [Inaudible] [00:32:26] JS: Yeah. I wanted to ask us one last question on on what you’re working on now and what you and your colleagues are looking at for future work. SN: Okay. So we have, at UCLA, we have this new center, it’s the UCLA Center For Critical Internet Inquiry. We’re part of a network of centers that have been funded by the Minderoo Foundation. And they include kind of critical technology centers at NYU, New York University, at Cambridge, at the University of Western Australia, us at UCLA, and some friends at Oxford, at the Oxford Internet Institute and one of the things that, I guess there is kind of two things we’re all thinking about, which is how to strengthen the research so that people can access it and pick it up and touch it and do things with it. That’s so important. We’re thinking about public policy, what are the ways that we can inform policymakers with the best evidence based research because we think that’s what should inform policy, not just raw power. And we’re thinking about how to shift culture. So my colleague, Sarah Roberts at UCLA, she works, she’s kind of one of the world’s authorities on commercial content moderation. So we’re obviously thinking about policies that affect technology workers and the traumas that they experience in moderating content. That’ll always be core. And I’ll always be kind of working on algorithmic discrimination and oppression. But we’re taking up and trying to link arms, I think with other researchers around the world who also care about these issues. Most of the places that have studied the Internet and society have really been advocates for the tech sector, and have taken a lot of funds and resources from the tech sectors. So we’re kind of like, we’re on a shoestring compared to those places. But we don’t get big tech money into the center, because we don’t want it to influence the research we do. And we think that gives us a space for people who are interested again, in studying the most vulnerable and those who are most harmed and we need help and support. So people who are interested in that kind of work, again, should link up with any of these places where this work is being done. JS: Yes, absolutely. And I’ll put links to the center to your work so that people can check that out. Safiya, thanks so much. This has been really interesting. The book is great. Your work is great, and I really appreciate you taking the time to come chat with me. SN: Thanks so much, Jon. It’s really my honor and my pleasure, appreciate you. JS: Thanks to everyone for tuning in to this week’s episode of the podcast. I hope you will check out Safiya’s book and her work at UCLA. I have linked to all the things that we talked about in the episode in the show notes. If you’d like to support the podcast, please tell your friends and colleagues about it, write a review on iTunes or head over to my Patreon page. So okay. Anyway, until next time, this has been the PolicyViz podcast. Thanks so much for listening. A number of people help bring you the policy of this podcast. Music is provided by the NRIS. Audio editing is provided by Ken Skaggs and each episode is transcribed by Jenni Transcription Services. The PolicyViz website is hosted by WP Engine and is published under WordPress. If you would like to help support the podcast, please visit our Patreon page. The post Episode #183: Safiya Noble appeared first on Policy Viz.
48 minutes | a month ago
Episode #182: Aaron Williams
39 minutes | 2 months ago
Episode #181: Virginia Eubanks
How does technology, data, and race intersect? On this week’s episode of the show, we talk about how high-tech tools and software profile and punish people of color and low-income people and families. To help me better understand these issues, I speak with Virginia Eubanks about her book, Automating Inequality, and how technology and digital industries perpetuate a permanent underclass. Virginia Eubanks is an Associate Professor of Political Science at the University at Albany, SUNY. She is the author of Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor; Digital Dead End: Fighting for Social Justice in the Information Age; and co-editor, with Alethia Jones, of Ain’t Gonna Let Nobody Turn Me Around: Forty Years of Movement Building with Barbara Smith. Her writing about technology and social justice has appeared in Scientific American, The Nation, Harper’s, and Wired. For two decades, Eubanks has worked in community technology and economic justice movements. She was a founding member of the Our Data Bodies Project and a 2016-2017 Fellow at New America. She lives in Troy, NY. Episode Notes Virginia’s website Virginia’s books: Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor Digital Dead End: Fighting for Social Justice in the Information Age Ain’t Gonna Let Nobody Turn Me Around: Forty Years of Movement Building with Barbara Smith Cathy O’Neil, Weapons of Math Destruction Dorothy Roberts at the Harvard Law Review Our Data Bodies Project Data4Black Lives Ella Baker Center iTunes Spotify Stitcher TuneIn Google Podcasts Support the Show This show is completely listener-supported. There are no ads on the show notes page or in the audio. If you would like to financially support the show, please check out my Patreon page, where just for a few bucks a month, you can get a sneak peek at guests, grab stickers, or even a podcast mug. Your support helps me cover audio editing services, transcription services, and more. You can also support the show by sharing it with others and reviewing it on iTunes or your favorite podcast provider. Patreon Transcript Welcome back to the PolicyViz podcast. I’m your host Jon Schwabish, and on this week’s episode of the show, we’re going to be talking about how high tech tools and software profile and punish people of color and low income people and families. And to discuss these complex really interesting issues, I chat with Virginia Eubanks who is an associate professor of political science at the University of Albany in New York State. Virginia is also the author of the 2017 book Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. Now, if you haven’t read Virginia’s book, I really, really highly recommend it. Some of the stories she tells, and she does a really good job of weaving in the data with the stories of people and places, a topic that comes up again in this podcast, is just amazing for me. For me, the book is right up my alley, it’s a mix of public policy considerations, data considerations and technology consideration. So it’s right in that sweet spot. Virginia and I also talk about a lot of groups doing amazing work in this space including the Center for Media Justice, Data 4 Black Lives and the Ella Baker Center, all of which I will link to on the show notes page. And I’m pretty sure you’re going to learn a lot on this week’s episode of the show, so here’s my discussion with Virginia. Jon Schwabish: Hi Virginia, welcome to the show. Thanks for taking time out of your schedule. Virginia Eubanks: Yeah, thanks so much for having me. JS: I’m really excited to chat with you. I really, really enjoyed your book, Automating Inequality. There seem to be a bunch of these books out right now, but the thing that struck me about your book and that I hope we’ll spend some time talking about is you weave in not just the numbers and not just the technical parts of what’s happening but also with the stories and how it impacts real people and real families and real communities. And so, there’s a lot of books out there that I think are a little more academic and your sort of really, for me at least, struck a chord. I’m excited to chat with you about it. I thought maybe you could just talk a little bit about yourself and your background and why you decided to write this particular book on this particular topic. VE: Yeah. So I’m really glad to hear that the book spoke to you in this way, because it just has always seemed really obvious to me that algorithmic and justice or digital decision making or whatever it is you want to call it right now, particularly in public services, it’s all about people. Right? It’s all about people, and it’s all about politics. And one of the things that I get really concerned about when we talk about these issues sort of in public is that we sometimes frame them, these new technologies, just as issues of sort of administrative upgrades or efficiency upgrades, and so they’re not political in themselves. But one of the arguments I try really hard to make in the book is that these technologies are political decision making machines. And in fact, the thing that is sometimes most worrisome about them is that they’re sort of politics pretending they’re not politics. So this great political scientist that I love, named Deborah Stone, is writing a new book about numbers, and one of the great lines in the book and she says numbers are just stories pretending they’re not stories. And that’s very much sort of the approach I took to this work which is like there’s so much great work out there including sort of earliest, in some ways out, Cathy O’Neil’s wonderful book, Weapons of Mass Destruction. One of the things is so strong about that book is she’s a quant person herself, she’s really good writer, and so she makes it really clear how the technology works and what the impacts might be. But I felt myself after reading it as much as I love the book, really hungry to hear from the people who were being affected, and that goes way, way back in my history. So the moment I think of as the sort of origin story of this book is all the way back in 2000, I was working on a project with a group, a community of women who lived in a residential YWCA in my hometown of Troy, New York, and we were working together around issues of sort of technology and economic inequality. And the sort of idea that was really current at the time was this idea of the digital divide, this idea that the sort of most important social justice issue or one of the most important social justice issues of the digital age was the issue of lack of access, whether that was along racial lines or that was along class lines or gender lines. And so, I kind of went into this collaborative project in the late 90s with this in my head, this community of wonderful women at the YWCA, really sort of eventually just sat me down and like forced me to have what we in the south would call a come-to-Jesus moment around my assumptions and basically said, look Virginia, we don’t lack interaction with technology in our lives, it’s just the interactions we’re having with them are terrible, are really exploitative, make us feel unsafe, make us feel vulnerable. And one of those moments which I recount very briefly at the beginning of this book feels very much like the seed of Automating Inequality, and that was I was talking to a young mum on public assistance named, goes by a pseudonym in the book Dorothy Allen, and we were talking about her electronic benefits transfer card, her EBT card which is the sort of ATM like card you get public benefits on in most places now. But they were pretty new in 2000, so we were talking about it, and she said I was asking you different questions about how it was working for her, and she said, right, maybe there’s some ways that the stigma is a little bit less than pulling food stamps out in the grocery store, but frankly, most of the tellers don’t know how to use them, so they just shout, like, food stamps, how do I deal with this card. So not that much less stigma. In some ways it’s more convenient, yeah, I guess. But in reality, the thing that really stands out to me, she said, is that when I go to see my caseworker, all of a sudden she’s asking me questions, like, why are you spending all this money at the convenience store on the corner, don’t you know it’s cheaper to go to the grocery store. And so she sort of pointed out that this digital record that was being created by her electronic benefits transfer card was creating a trail that her caseworker could follow to track all of her movements and all of her purchases. And I must have had this incredibly naively shocked look on my face, I was, I don’t know, 25 at the time, and had only been on one public benefit program in the past and not on food stamps. And so, she kind of looked at my face and laughed at me for a while, and then, like God, actually really sort of quiet and concerned, I was like, oh Virginia, you all, meaning, I believe, at the time, meaning sort of professional middle class people, I was a graduate student at the time, like, you all should be paying attention to this because you’re next. And that moment has always stuck in my head not only because I think that was actually incredibly generous of Dorothy, of her being like, oh we’re dealing with this shoot storm, and you all should be concerned because it might impact you too. But also, it stuck in my head this idea that the folks who are sort of on the cutting edge of a lot of the most intrusive invasive digital surveillance technologies are poor working class people, and you need to go to the source to ask people how those tools are operating in their lives. And so I was really committed writing Automating Inequality, you know, I talked a lot, I did more than a 100 interviews for the book, I talked to lots of different people, I talked to designers, I talked to policymakers, I talked to cops, I talked to frontline social workers, I talked to welfare case examiners, but in every case I started by talking to the people who felt like they were the targets of the system I was describing. So in Indiana, it was folks who either struggled to keep their benefits or lost their benefits during that benefit modernization. In Los Angeles it was unhoused folks who had interacted with the coordinated entry system and either it had gotten them housed and it was often a happy story or they had been shut out somehow. And in Pennsylvania, in Allegheny County story, I started with the families who felt like they were being targeted by this algorithm that risk rates their parenting based on the potential risk to their children of abuse or neglect. And it just turns out that these stories of these magic new digital tools look really different from the point of view of the targets of those tools. JS: Right. VE: Yeah, I think it’s just really crucial to start with impact, start with who does it matter to and how’s it affecting their real lives every day. JS: Yeah, I mean, a lot of what you’re pressing is this idea of empathy through the storytelling, right – being able to put ourselves in someone else’s shoes and say, what if I was the person on snap receiving benefits and having to use this card, what would my experience be like, and maybe that’s something that we’ve lost a little bit over the last, let’s say, three years or so. VE: Yeah, in a sense, I mean, for me, where that instinct comes from is less about empathy and more about fact. So the old saw is the future has already arrived, it’s just unevenly distributed, this is something that’s widely said, that William Gibson said in the sort of 80s, and I think he meant it slightly differently than I do. I think he meant it that the newest, flashiest technology go to wealthier, more powerful people first. I think in the kinds of cases I’m talking about that these tools are tested first in communities where there’s sort of low expectation that people’s rights will be respected. And so where you see the sort of most bald faced uses of these tools tend to be in these communities, not just poor working class communities and not just communities where people are using public assistance, but I do think that that’s an important place to look, but also migrant communities, communities of color, First Nations, indigenous folks interact with these tools in really different ways than non-native people do. So it’s really about not projecting potential harm of these tools into the future, like, the example would be, and this is actually really important, so let’s talk about, which we do a lot, what an autonomous car would do if it came on in the road upon a box of puppies and a bicyclist, which one would it hit. That’s actually an important question to ask. We should be asking that question. But we have this tendency, when we talk about technology and policy to talk about the problems that might come in the future instead of just going and talking to people about what’s actually happening in their lives right now. And so that just tends to be my approach which is like these future problems are interesting and in some ways sort of beautiful puzzles that people like to sort of grapple with in their heads. But if we want to get real about what’s actually happening we have to go ask people and we have to go ask people in these places where there are real expectations that people’s rights will be respected. JS: Right. So dealing in the now so that we could deal with the future, we can evolve to the future that we want to get to. VE: Exactly, yeah, exactly. I think that’s a great way to put it, yeah JS: So one of the examples, and it’s early in the book is this, you just mentioned this experience in Indiana where the public services mostly TANF, I believe, and food stamps and Medicaid, they were trying to modernize the system, there’s a lot of the technology issues that you talked about throughout the book. I wanted to ask a lot of what you talk about in the book and a lot of what people in the world talk about is a lot of these monetization efforts are about efficiency, they’re about cost cutting, they’re about – and sort of privacy sort of gets a little bit of a wink and a nod of what I see, and I’m just curious how should we as both consumers or people who are receiving these benefits or involved in these programs or just as citizens, so how should we think about these competing incentives because there is a budget constraint for some of these programs, and yet we have these, what I’ll call after reading your book, these various these fairly scary outcomes that are possible? VE: Yeah. So I think that idea is that we have to work with resources that are limited beyond our ability to change them is one of the most common reasons people will give you for going to these digital tools. So there’s generally two sort of first run reasons that people give for these digital tools. The first is efficiency, cost savings, and sometimes the identification of fraud, waste and abuse. The second is ferreting out legacy patterns of discrimination in frontline decision making. And both of them are reasonable. We want there not to be frontline discrimination in decision making. We want rules to be applied the same way in each case in most cases that we can talk about that some more in a minute because people are individuals and their problems and needs and resources are different. But let’s just talk about the efficiency issue, the triage issue. So even though I spoke to, like I said, 100 people, and I spoke to lots and lots of designers, and all the designers were quite different in their approach, in their politics, and what they thought the problem was – to a person, every single one of them would say that they had to use the tool because it was necessary to do a kind of digital triage, that there weren’t enough resources for everyone, and that in the absence of having sufficient resources they had to make really hard decisions about who should get access to benefits and who shouldn’t. And one of the things that I try to raise in the book is this idea that triage actually isn’t an appropriate way to talk about programs that have been relentlessly defunded. So when you talk about public services, for example, since 1996 and actually even before, we’ve made a series of really consequential political decisions to defund our public service system. And you can’t then say, oh let’s relate this to like a natural disaster. We have to do triage because how could we know that this tsunami was coming, and we don’t have enough medicine. Right? This is clearly not what’s happening. And so one of the arguments I make in the book is that it’s actually not appropriate to use the language of triage, because if the problem is not temporary and if there are not more resources coming, then what you’re doing is not actually triage, it’s digital rationing. And so if it’s digital rationing, let’s name it, let’s say that’s what we’re doing and have a conversation about that. I find this idea that like, oh we have to do it, because we just don’t have enough resources, I find that specious, I find it really unconvincing and potentially mal-intentioned. And you see that across all three of the cases, that’s the same argument that’s made in Indiana and Los Angeles and in Allegheny County. It’s just that there’s just not enough resources. But you end up with these more thorny or more awful problems by defunding these programs at the front end. So for example, if you look down the road of the book towards the Allegheny County case, so we’re talking now about building a tool that is supposed to risk rate all the families in Allegheny County based on their potential to maltreat that is abuse or neglect their children in the future, so that they can be investigated by the children youth and families, administration of children youth and families there, with an eye for potentially pulling children out of the home and putting them in foster care. The reality is 75% of children who are put in foster care across this country are put in foster care because of neglect, not because of emotional, physical or sexual abuse, and that neglect is basically, the textbook definition of neglect is very similar to just being poor. It means not having safe housing, it means not having enough food, it means having to leave your child alone or with someone who’s not terribly trustworthy because you have to go to work, and all of those are downstream problems from not funding public services. So I find it so sneaky that you then say, oh but we have to do this because we don’t have enough resources to investigate all these dangerous families, when the state is making those families dangerous, it’s not parents who are making the families dangerous, it’s the state. And I mean, even in a very practical way, in Indiana, if this was about cost savings, it did not work because they signed out what was originally a $1.16 billion, 10-year contract, it ended up being a $1.34 billion with a B contract to create a system that basically worked to deny people public assistance, worked so badly that the community rose up and shut it down three years into a 10-year contract. And then IBM turned around and sued the state for breach of contract and originally won, like, won damages on top of the money they had already collected. And if you had just looked at the contract with an eye to how public services actually work and what the impact might be on affected communities, you could tell from the contract out what was going to happen, like, all of the metrics were nothing was like whether or not people got benefits they were entitled to, nothing it was about whether the decision that was made was correct. All the metrics were how fast did you get off the phone, and how many cases did you close. And so you absolutely, I mean, you could have known from the beginning that that was going to be the effect. JS: Yeah. So that the metrics that they’re looking at are efficiency but not necessarily the usefulness of the program and certainly not to the people who are participating in the programs. VE: Well, the metrics are short term efficiency, the metrics are like how many people can we get off public assistance this year, and they certainly wouldn’t say that was the metric but I think you could read between the lines of the contract pretty easily that that’s actually the metric. But that just creates, like I said, all of these downstream problems. And I’m not even talking about the human costs, people like Omega Young who lost her Medicaid because she missed a phone appointment because she was in the hospital dying of ovarian cancer. So I’m not even talking about the human effects or the political effects of a community that now will not trust public service programs because they’d have these god awful experiences of being sort of digitally surveilled. I’m not even talking about those, I’m just talking about the straight money, they just lost money on that bet. And I mean, and that’s not even like the legal case that the state had to engage in to fight back against IBM suit that doesn’t include the hundreds and probably thousands of fair hearings that they had to hold when people were wrongly denied their benefits. So not even talking about the cost to people, like the actual cost, they lost money on that. JS: Right. I’m curious, so the book came out three years ago in 2017, which feels like 90 years ago right now. VE: It is really funny. It’s really true. JS: But I’m curious, if at all, how has your perspective changed over the last few months after the murder of George Floyd and Breonna Taylor and unfortunately so many others? How has your perspective changed on these issues especially over the last few months or has it not changed? VE: Yeah, so I think that you can’t talk about public benefits in the United States without talking about race and without talking about policing and the criminalization of poverty. So I think in this moment I might have framed what I said slightly differently, but I really think so much of the conversations were really exciting and important conversations we’re having about police brutality right now are absolutely clear and obvious in the cases in the book. So though white people still make up the majority of people on public assistance in the United States, perceptions about welfare as a sort of “black thing” impacts all of our policies and all of the ways things are implemented, I mean, everything from racial disparity and foster care to sanction rates in different states, that is sanctioning is throwing people off of public benefits because they’ve made a mistake. All of that is racially determined in some really serious ways. And each case that I talk about, Indiana and Los Angeles and Allegheny County, race plays a really significant role in the case. In Indiana, race played a really significant role on where they rolled out the system first as they were testing it, and actually what I thought was really interesting was that they’re really just a handful of counties in Indiana that have the majority of the Black and African American population, it seems quite intentionally rolled this system out to the counties that did not have Black populations first, which I think is really fascinating. I have some real suspicions that I couldn’t confirm that it might have to deal with sort of using racial resentment as a political tool. So I saw race at play very much in Indiana, and also in the case of folks like Omega Young who really faced the worst outcomes of the system, they were majority Black women. In Los Angeles, I look not just at Skid Row where many of the stories of the unhoused community come out of, and for good reason, it’s a huge and very politically active community; but I also look in South Central which actually has more unhoused people than Skid Row but gets much less funding and much less attention largely because of race. And in Allegheny County, I look at the way that past legacies of racial discrimination are used actually to rationalize the implementation of this tool and sort of the problems with saying that data is racially neutral which I think we’re all pretty familiar with now that we’ve had these sort of conversations about policing, like the way that we stop and frisk, use data, or the way that racism skews data I think is a conversation we’re much more comfortable having these days. So I mean I feel like that conversation was very much in the book. One of the things that I’m really excited about that has happened since the book is that there have been the sort of intentional linkages that we’ve started to build across different areas of policing, I think of like lowercase policing. So Dorothy Roberts wrote this really great review of the book for the Harvard Law Review that talks about the connections of what I call the digital poorhouse to what she calls the digital carceral state. And it’s exactly what I hoped would happen with the book is that we would start making these connections about how policing operates in different areas, not just in criminal justice and law enforcement, but also in child protection, also in public assistance, also in homeless services, that, as my really brilliant colleague Mariella Saba of Stop LAPD Spying Coalition says, policing wears many uniforms, but that these processes of policing show up in all of these different social programs, and how dangerous that is when you start to conflate economic support programs and law enforcement under the same data structure, under the same rubric and using the same people. I think that’s actually an incredibly dangerous thing. JS: Yeah. I’m curious, how do you see people, organizations out there trying to work and remedy the problems that you’ve identified – I mean, I feel like we can see police violence, we can see police officers pointing guns at young black men standing at a bus stop which is a story that came out this morning. But the issues that you’re highlighting are some more of these hidden forms of racism and structural racism and have you seen organizations working to turn things around, and how have they sort of gone about doing that? VE: Yeah, I mean, I think that’s one of the things that makes talking about these technologies so interesting and so important is that we sort of talk about technology particularly as a tool that is neutral that is like you can use it in sort of any old way, I think it’s much more useful to talk about tools as manifestations of structures. So of course the technology that we build for the foster care system is going to be racist, because in every single county in the United States there’s a problem with racial disproportionality in foster care and that’s affected all of the data, and that affects all of the machine learning, and that affects all of the outcomes of all of these tools. And so, it’s like this manifestation of the structural problems that we’re already facing, and I think you’re right though, I think that when we look at these technologies, the harm looks really different than the interpersonal conflict, like, police officer black youth. But the problem that we have is not in the United States is not racist cops, I mean, we do have that problem. The problems though are structural, are really deep, so even if we replaced every police officer tomorrow with Gandhi, we would still have a lot of these problems. JS: Still have, yeah. VE: And so that’s the thing about talking about the tech is that it allows us to have those conversations in a way that I think is really, really powerful. So yeah, there is some work going on. I’m probably out of the loop of the sort of newest most exciting work around that. I was involved after the, as the book was coming out and after the book with a really great project called Our Data Bodies project that is starting to sort of imagine what community safety looks like in a world of sort of digital surveillance, the Center for Media Justice – I think they changed their name recently but I don’t remember what it is, which is terrible, sorry guys, you’re awesome – have been doing that work. I think it’s not the kind of work that has data for Black Lives. But it’s not the kind of work that necessarily needs a whole slate of new organizations. It feels to me like it is a layer that we add to the organizing work we’re already doing. So if you’re interested in economic justice, you also have to think about algorithms now. If you’re interested in police brutality, you also have to think about CompStat. If you’re into electronic shackling, that it’s just it’s a dimension of the work that so many people are already taking on. And I think one of the most important things that I really hope people take out of the book is that none of these systems are inevitable. So Indiana is the perfect example. The state was like, you know what, we’re doing this, we don’t care what people say, we’re going to hold basically no public comment, period, on this like more than billion dollar contract, we’re just going to do it, and the citizens of Indiana shut that thing down – they just, old-school organizing, had townhall meetings, went door to door, handed out flyers, and they were just like, no, you don’t get to treat us like this. I think we’ve seen those kinds of successes around facial recognition technology, around tech workers refusing to work on projects that they find morally reprehensible. So we’re seeing that kind of work pop up all over the place. I’m really excited about that moment, the moment that we’re in. One of the things that I really still, I want to say again, because I so want to see it happen, is like this connection between the policing apparatus of law enforcement and the policing apparatus of the programs that we think of as more charitable or more helpful because we have a tendency to think that these tools are okay as long as they’re like “just helping and not punishing anybody”. But the reality is things like Child Protective Services plays both a helping role and a charitable role and a punishment role. And so, if we don’t see that as a policing system, we’re really in danger of giving it a pass around things that we would never accept in law enforcement. JS: Yeah. There’s this book by Zach Norris who runs the Ella Baker Center out in Oakland called We Keep Us Safe, and he expands on this exact point that a lot of what our public services and programs do is about punishment as opposed to trying to keep people safe and get them, you know, get people who need services to the right place where they can be successful in the long term as opposed to taking kids out of school, putting people behind bars, all sorts of other punishments that we inflict on people in our existing public service programs. VE: Yeah, well, I love the Ella Baker Center, they’re amazing, and I have heard about this book, I haven’t read it yet, but I’m really excited about it, because one of the things that sort of kept me out of the loop for the last couple of years is, as I started to write the book, my very, very dear and much beloved partner of many years, more than I care to admit, Jason Martin, was attacked and really badly beaten in our neighborhood and ended up suffering from a pretty horrifying case of post-traumatic stress disorder. And living with someone with PTSD, and after the book came out and things calmed down a little bit, we largely sort of turned our attention to his healing and maybe supportive to his healing. And one of the things that’s become really clear to me as a partner of somebody with PTSD and particularly as a partner of somebody with PTSD during a pandemic is how poorly that the systems we hope will work to keep us feeling and actually physically sort of safe and healthy, how routinely and sort of life destroying ways those systems fail us. And I am really interested in figuring out how to live in a world where we keep us safe, and that means something beyond dialing 911, that feels really important to me not just on an intellectual level but on a day to day, you know, leaving my house to go to the corner store level. So it is that kind of safety or security, particularly community security, feels so crucial to me right now, and the pandemic just makes that all the more obvious. The pandemic has made it so clear how harmful these cracks in the system are to everyone, I mean, especially to the people who have already been suffering from falling in the cracks before the pandemic, but the pain has I think been widespread enough through the pandemic that a lot of people are starting to wake up to the cracks in the system. JS: So I want to ask you one last question on that exact note. So you’ve talked sort of both an optimistic and a pessimistic tone that we are seeing these cracks and there’s a lot of work going on to improve the system and so on. I wonder, going forward, and I know we’ve already talked about, let’s not just go into the future, but looking ahead, are you optimistic that things will change for the better, and probably most of us are feeling sort of down and pessimistic these days, but on the things that we’ve been talking about, are you feeling like things are heading in the right direction or they’re heading in the wrong direction? VE: So that’s a great question, like, do I think things are getting better or worse, I’m going to say both. And I know what a frustrating answer that is. So I have, for a long time, I have identified myself – and this comes out of the work I did at the YWCA back in the early 2000s – I’ve identified myself as a hard one optimist, in that I feel very aware of the real life shattering system destroying potentially world ending catastrophe we seem to be teetering on the edge of. And also, like, feel like I see so much in the social movements I’ve been part of, and in the community I live in, and in the work that I’ve just been honored to do with all sorts of different folks but primarily with poor and working class communities, I just feel so honored to be in the presence of our continued optimism that we have hope and we can create change, and that out of this mess that we’re in we can birth the kind of world we want to live in. So I don’t think I’m naively optimistic, but I also feel like hope is part of revolution, like, you have to live – Barbara Smith who I did a book with many years ago is a black feminist, who is a great hero and friend of mine, she says, for the revolution to happen you have to live as if the revolution is possible always. And I think, for me, that that feels like a final word. JS: Yeah. Well, I think that’s a great place to stop. VE: I always try to give Barbara the last word. JS: That’s a good quote to end on right there, yeah. VE: Yeah, that’s right. Well, thanks so much for coming on the show. It was great. JS: Thank you so much for having me. Thanks everyone for tuning in to this week’s episode. I hope you learned a lot, I trust you did. If you would like to support the PolicyViz podcast, please consider sharing it on your networks, on Twitter, or leaving a review on your favorite podcast provider platform. Or you can go over to my Patreon page and support the show financially. For just a few bucks a month you get a coffee mug or you get my thanks or you get a note from me every month giving you the heads up on what’s coming up on the podcast. So until next time this has been the PolicyViz podcast. Thanks so much for listening. A number of people help bring you the PolicyViz podcast. Music is provided by the NRIS. Audio editing is provided by Ken Skaggs. And each episode is transcribed by Jenny Transcription Services. The PolicyViz website is hosted by WP Engine and is published on WordPress. If you’d like to help support the podcast, please visit our Patreon page. Credits Music by The NRIs Audio editing by Ken Skaggs Transcription by Jenny Transcription Services PolicyViz.com is hosted by WPEngine and run on WordPress Photo by Taylor Vick on Unsplash The post Episode #181: Virginia Eubanks appeared first on Policy Viz.
33 minutes | 2 months ago
Episode #180: Zach Norris
Zach Norris is the Executive Director of the Ella Baker Center for Human Rights, author of We Keep Us Safe: Building Secure, Just, and Inclusive Communities, and co-founder of Restore Oakland, a community advocacy and training center that will empower Bay Area community members to transform local economic and justice systems and make a safe and secure future possible for themselves and for their families. Zach is also a co-founder of Justice for Families, a national alliance of family-driven organizations working to end our nation’s youth incarceration epidemic.Zach helped build California’s first statewide network for families of incarcerated youth which led the effort to close five youth prisons in the state, passed legislation to enable families to stay in contact with their loved ones, and defeated Prop 6—a destructive and ineffective criminal justice ballot measure.We Keep Us Safe, released in 2020, has been praised by Forbes, the San Francisco Chronicle, the Boston Globe, and Kirkus Reviews.In addition to being a Harvard graduate and NYU-educated attorney, Zach is also a graduate of the Labor Community Strategy Center’s National School for Strategic Organizing in Los Angeles, California and was a 2011 Soros Justice Fellow. He is a former board member at Witness for Peace, Just Cause Oakland and Justice for Families. Zach was a recipient of the American Constitution Society’s David Carliner Public Interest Award in 2015, and is a member of the 2016 class of the Levi Strauss Foundation’s Pioneers of Justice. Zach is a loving husband and dedicated father of two bright daughters, whom he is raising in his hometown of Oakland, California.” In this week’s episode of the podcast, Zach and I talk about his work in and around Oakland, what the term restorative justice means, and how data analysts can do a better job talking with people we study and write about. Episode Notes We Keep Us Safe: Building Secure, Just, and Inclusive Communities Ella Baker Center Just Mercy by Bryan Stevenson Who Pays? The True Cost of Incarceration on Families | Report from the Ella Baker Center More books on race and equity iTunes Spotify Stitcher TuneIn Google Podcasts Support the Show This show is completely listener-supported. There are no ads on the show notes page or in the audio. If you would like to financially support the show, please check out my Patreon page, where just for a few bucks a month, you can get a sneak peek at guests, grab stickers, or even a podcast mug. Your support helps me cover audio editing services, transcription services, and more. You can also support the show by sharing it with others and reviewing it on iTunes or your favorite podcast provider. Patreon Transcript Welcome back to the PolicyViz podcast. I’m your host Jon Schwabish. On this week’s episode of the show, we turn to thinking about community development, social justice and how we, as data analysts and people who visualize data, how we can talk to actual people to learn about their lived experiences. Now personally, as a quantitative economist, I’m familiar with asking a question, finding the relevant data, downloading it, analyzing it, and then writing about it. But I’m not as familiar with talking about the people I’m studying or the people I’m communicating with. So if I’m doing a research project on say child nutrition issues, I’m not as good at reaching out to school officials or families or students to better understand their experiences or their challenges or even what they think could be a policy solution. But Zach Norris, the guest on today’s show has extensive experience talking with people. Zach is the executive director of the Ella Baker Center for Human Rights. He is the author of We Keep Us Safe: Building Secure, Just, and Inclusive Communities, and he’s also co-founder of Restore Oakland, a community advocacy and training center that empowers Bay Area community members to transform local economic and justice systems and make a safe and secure future possible for themselves and for their families. I first met Zach through a meeting at the Urban Institute and his book has helped me refine my thinking about how we as a society and we as communities can shift the conversation about public safety away from fear and punishment and toward growth and support systems for families and their communities. So I hope you’ll enjoy this week’s episode of the podcast. Instead of talking specifically about data visualization techniques and data analytic techniques, what here I’m focusing on and thinking about is how is we as data analysts and data visualizers can do a better job of reaching out to the people that we are studying and the people that we are communicating with. So here’s my discussion with Zach Norris. Jon Schwabish: Hey Zach, how are you doing? Thanks so much for coming on the show. Zach Norris: Of course jon, I really appreciate it, happy to be able to talk with you. JS: It’s great. I’m really excited to talk about more of your work, we’ve been talking over the last few weeks, and I’m really excited to hear more about your work, the Ella Baker Center, and of course your book. I wanted to start by giving you just a chance to talk about your background, talk about the book maybe a little bit, and talk about the work that you’re doing now as the executive director at the Ella Barker Center. ZN: It’s the Ella Baker Center. JS: Baker? I see. I got Barker, I don’t know why, yeah, Ella Baker makes more sense, yeah. ZN: No worries. I am happy always to talk about Ella Baker and Ella Baker Center. I was born here in the Bay Area in San Francisco. We moved to Oakland when I was a week old, I sometimes say it was a week too late just because I love Oakland that much [inaudible 00:03:08] to San Francisco. But growing up in Oakland, in East Oakland, I was sheltered from some of the things that were happening around me, namely, the kind of rise of the school to prison pipeline. I grew up in the 90s, went to high school from 1991 to 1995, and during that time youth crime was actually declining in California but the number of youth crime stories was going up by some 700%, as I understand it. So if you were seeing a crime occurring once per week, youth crime story once per week in ‘91, you’re seeing it more like once per day by ‘95. That was impacting policy across the country and here in the Bay Area, and luckily, I made it out of high school being a light skinned African American kid who went to Catholic school and had a set of privileges that many of my peers growing up in East Oakland didn’t. And so, by the time I got to Harvard as an undergrad, I started seeing young people doing things that young people do, from using and abusing drugs to getting into fights on a typical adolescent behavior, but at Harvard you got time off, you got counseling, you got resources and support, it was always clear that young people were more than their worst mistake, as Brian [inaudible 00:04:29] says. But in East Oakland, my family and friends were getting locked up for doing some of the same things and losing years out of their lives incarcerated effectively. And so, that kind of disparity is what led me to an interest in social justice, I found the Ella Baker Center as a law school student, and really was hooked from the start because we were working on issues challenging the school to prison pipeline and just this dynamic of young people being criminalized from such an early age and that really is what led me to work at the Ella Baker Center. JS: Can you talk a little bit about the actual work at the Ella Baker Center and what you’re all doing – I know you do a lot of local work in Oakland and Alameda County and I’m curious both about the work sort of just generally and how important it is but also how you work at the local level to effect change. ZN: Yeah, I mean, we start with principle and understanding that everybody is an agent of change. Right? That like Ella Baker believed in the power of ordinary people to make change and so do we, so we advanced what we call a books not bars, jobs not jails, healthcare and housing not handcuffs agenda. So we love alliteration and we also love fighting for public health solutions to what we identify as public health issues. And so when I started we were involved in a campaign to stop what would have been the largest per capita juvenile hall in the country from being built here in Alameda County, we called it a super jail for youth. And over a couple of year-long campaign we were successful in moving Alameda County to drastically decrease the size of the juvenile hall and to increase support for families and young people using some of the resources saved. And guess what, the juvenile hall is still way under capacity today, and we still have a lot of work to do to really ensure that young people, especially youth of color, get the kind of public health interventions and supports that they and their families need. But it has been campaigns like that to really call attention to the way in which local governments, state governments, national governments have been perfectly happy to throw money at what we believe are failed intervention – the number one predictor of adult incarceration is youth incarceration and youth justice system involvement. So why not try to do something different for young people? That is at the heart of our work but we also work with folks who are in the adult system as well, we use policy, advocacy, media. We’re in Sacramento talking to legislators. We work with folks inside of San Quentin prison to actually develop different policy ideas. We take those to Sacramento, we actually engage people in San Quentin and other prisons across the state to lobby legislators to send letters to be in communication with legislators. Once we pass those bills, we actually engage people in leveraging and using those bills to help them get out of prison, so it’s really from the start to the finish and engagement with the community folks both inside a prison and their family members and formerly incarcerated folks as well to really develop the solutions that we know work for everybody. So that’s what our work is about. JS: So it’s interesting the way you frame a lot of your work is going into the prisons, talking in community groups and having these discussions, and one thing that I love about the book is you weave in those stories along with the data, so the way we started this conversation was this change in youth imprisonment in the early and mid-90s. And I guess, I have two lines of questions I want to ask, one I want to talk about is how you actually conduct these conversations, and I think for data analysts, that’s a tough thing to do. But the first one I want to talk about was when you were pulling together the book, you spent a lot of time especially right at the very beginning saying this book is going to be stories throughout, but it’s not just stories, you have a lot of data and facts behind it, and I’m curious how when you were writing the book and thinking about how you wanted to get your message across, you thought about combining and weaving these two pieces together sort of quantitative part and then the storytelling part. ZN: Yeah, thanks for that question Jon. I mean, I believe that stories are what motivate us and move us and are the things that we remember. And what really makes it possible for people to take action is their understanding of a story and their understanding of their own story and that they can make a difference in their lives. And so, for me, it was kind of a no brainer that the book would really highlight the stories of folks that I’ve worked with, the book is called We Keep Us Safe: Building Secure, Just, and Inclusive Communities, and really is about how we move from a framework of fear to a culture of care. And rather than seeing people who have broken the law as being simply deserving of punishment really, understanding that all of us are more than our worst mistake, and that if we adopt approaches to public health issues, that will not only benefit the person who’s caused harm, it’ll benefit the person who’s been harmed, and it’ll benefit the community as a whole. And so, what I tried to do with the book is really weave in stories that I’ve come to know through working with formerly incarcerated folks, through working with their family members, but also through doing community driven research. We did a report called Who Pays? The True Cost of Incarceration on Families in 2015, and it was a major project, it involved 20 community based organizations across the country, we worked with research action design collective that specializes in kind of community driven research and it was formerly incarcerated folks, their family members, currently incarcerated folks, survivors of crime who really developed the interview protocol, the focus group protocol who helped develop the survey protocol, and we took that out into multiple states across the country, multiple communities, and it was the folks who were impacted themselves who did that research. It caught a lot of attention, the Washington Post, New York Times, among many other outlets, picked up the Who Pays report. But surprise-surprise, the media focused more on the problem than the solutions that we were proposing, and so the idea behind the book was really to weave in the narratives around what are the problems but also to lift up the solutions, and the second half of the book basically almost does like a Choose Your Own Adventure kind of thing, if you remember those books when we were… JS: Yeah. ZN: [inaudible 00:11:53]. It says, here are three stories that unfortunately end in tragedy because we’ve adopted this framework of fear, what if we actually use public health approaches in these instances, how might these family stories be different. And I hope that it’s an engaging way to engage people in really thinking about their own story in addition to the stories that they’re reading because there are all kinds of choice points in our own lives where we make mistakes, and I think if we really are circumspect around our own race, class, gender and other kinds of privileges, we might find that, hey, someone was there for me when I made a mistake and shouldn’t that be available to all of us, yeah. JS: Right. I mean, it’s very interesting because I love the Choose Your Own Adventure idea, but in those stories, towards the end of the book, you have these, you know, it’s the choice points of individuals, but it’s also the points within the criminal justice system and in the social support system that local and federal governments run that they all make decisions at certain points too and policies that encourage or discourage certain behaviors or certain punishments. So it’s interesting how these, they kind of branch out in all these different ways, into these different end points. ZN: Yeah, I mean, if you look at the story of Alan Feaster and Darelle Feaster, Alan’s the dad, Darelle is the teenage son who’s messing up in school and skipping school. His dad is like, I went through the military, I’m struggling, I’m trying to provide the right direction, reaches out to the juvenile justice system. His kid gets involved and gets into a group home hours away, he and a friend steal a car trying to get back home just stocked in there literally I think like five or six hours from home, and then gets into a youth prison where he spends the greater part of 18 months on solitary confinement for 23 hours a day. And he and his cellmate, different kid, ended up committing suicide according to the report. His father still didn’t believe that that was the case, but – and whatever happened, it ended in tragedy [inaudible 00:14:16] talk about not only what happened to Darelle but also what happened to Alan as an African American male who didn’t get the kind of healthcare services and support that I believe he deserved, and he ended up dying years later. And I think that in each of their lives, there were opportunities to provide them support individually that would have had an impact for the whole family. And it’s because of structural racism and classism, and also just because we’ve really just grown so many resources behind policing and punishment that it’s really come to overshadow and crowd out the kind of basic common sense approaches that I think other countries really kind of take for granted. JS: Yeah. So I want to make sure that people understand the highlight of that – or not the highlight of the story, but where that story starts is that the father reaches out to the local law enforcement criminal justice system for help. By doing that, the son gets involved in the criminal justice system as opposed to getting assistance, mental health counseling, financial planning counseling, you know, what other services they would need to get them both on this path, it sort of leads them down this terrible path towards the young man committing suicide, and I think it’s just important for people to know who haven’t read the book, and I really do recommend that everyone should read it that these decision points are not just our own as individuals but also what happens when we seek assistance from our governments and from our local authorities. And all these decision points, they all come into play and affect these paths that we end up going down. ZN: Yeah, absolutely. JS: You talk a lot in the book about this phrase, restorative justice, and you define restorative justice, and I want to make sure that you get a chance to talk a little bit about it, because I think – and we’ve talked about this in the past that maybe people have a particular view of what that means that’s not exactly true or doesn’t really embody the essence of what restorative justice means, and you spent a lot of time talking about it in the book. And so I was hoping you could talk a little bit about it for folks who may not be familiar with this particular phrase in this particular area of the criminal justice system. ZN: Yeah, restorative justice is about holding people accountable while still holding them in community, and you almost have to take a step back before explaining restorative justice and just ask people to think about a time when they felt safe, and so I do that all the time because when I ask people what was the time when you felt safe, oftentimes people say, well, I was with my family or I was in my faith community, I was surrounded by these people, these are times when I felt safe. And so, there’s this connection between safety and relationship that safety is really intimately tied for human beings about being in right relationship. I think about how my grandmother held my hand, she held my hands with two hands, like one hand to hold my hand, the other hand would just kind of tap my little hand and remind me, hey, I got your back, but also I got your back if you make a mistake. She would hold me accountable but still hold me, and this is fundamentally divorced from the way we think about public safety which is really in this country tied to punishment in prisons. And so, when we remove someone from the community, we really sever ties and we make it much harder for people to be held accountable, because in order to be accountable to someone, you actually have to answer to them, but you can’t answer them if you’re not in dialogue with them and working to make them amends. So restorative justice is really about two people, and it’s kind of boiled down essence, it can be about two people, one who’s been harmed, other who’s caused harm, surrounded by the people that support them and a facilitator comes, facilitates a conversation on how is this person who’s caused harm going to make amends, how are they going to make it right. And accountability plan is developed which might involve them paying back the victim of the crime, it might involve them doing community service that is particularly geared towards supporting this victim. But what’s beautiful about it is that everyone in that circle who’s supporting those two individuals are also asking themselves what could I have done differently to prevent this harm from happening; and in that way is really building community accountability for people to think about, what’s going on in our neighborhood, what’s going on in our city, and how can we make that different. And that piece of it is really fundamental now because we see the way in which people in power also need to be held accountable, and so restorative justice can help build the muscle within our communities to actually hold more powerful people accountable. The circle isn’t going to look the same as a kind of individual harm circle, it might look different. But the beautiful thing about restorative justice is that it is applicable not just on the individual level but at the level of cities and even at the level of nations. Truth and Reconciliation processes in South Africa and other places have fundamentally been restorative justice processes. JS: Yeah. You tell a couple of stories about this process. Can you talk a little bit, maybe about your experience, either I assume witnessing one of these conversations, and I mean, these are difficult conversations to have, and what does that look like, what does it feel like when you are experiencing these discussions? ZN: Yeah, I mean, it is a powerful thing to witness because what typically happens in our criminal court system, as I mentioned, is people are kind of removed from the community, they don’t have the opportunity to hear. But what I’ve come to understand is that when a young person or any person is really forced to hear from the person they’ve harmed, to understand that, hey, I’ve been up at night, I can’t sleep, I still think about this incident when you came and snatched my laptop or pushed me down and then put my laptop away, like, those are things that still stick with me. And to have that understanding of the harm that has been caused but also to know that there are people who are there to support you in doing something different, because it’s one thing, for a young person or any person to have an aha moment where they’re like, yes, I understand the harm that I’ve caused, it’s another thing for them to be in a different economic situation where they don’t feel the need necessarily to take such drastic measures; and especially, right now, where there is such dire straits economically, I think it behooves us to really think about let’s get to the root of these problems, let’s really think about what makes it such that a 16-year-old, a 17-year-old or anyone is really put in a situation where they are forced to steal. My partner is working with restaurant workers, and when the pandemic hit she helped to raise millions of dollars to support restaurant workers who had recently lost their jobs. And the first wave of emails she got was thank you so much, and we really appreciate this, I don’t know what I would do. The second wave of emails started to come in and it’s like, well, I’m not sure how I’m going to continue to feed my kids, I’m trying to provide them with [inaudible 00:22:37] and bread from the food bank, it’s not enough, I’m worried that I may not even be able to continue to communicate with you because I’m going to lose my internet, I’m worried I may have to steal to try to support my family. And so people are in such dire straits and meanwhile the millionaires and billionaires are getting continued kickbacks from the government. And so what I tried to do in the book is not just talk about crime but really talk about harm in the broader sense in our society, in the ways in which we can be addressing these broader harms and in ways that will actually build greater safety for each and every one of us in this country. JS: Right. The other thing I wanted to ask you about was your interactions and conversations with people. So you’ve spent almost your entire career actually going into communities and talking with people and having these hard conversations. I think for a lot of people who work with data, who analyze data, especially the quantitative people, talking to people is not something that we are accustomed to doing. There’s a lot of reasons why we should be talking to the people that we’re studying or the people that we’re communicating with and part of that can be just having a more diverse and racially equitable awareness of what we’re working on, but I was hoping you might be able to talk a little bit about, I mean, I’m not even sure what the question is really, but how should researchers or data analysts think about having these conversations or having conversations with the people they study and how they should go about doing it, and maybe some things that you keep in mind when you’re going out and working with focus groups or working with community groups and working with people in and out of prison, what are those conversations like and how do you approach it when you’re having those discussions? ZN: Yeah, I mean, I think the first thing that I would say is that you don’t even know the right questions to ask if you’re not in conversation with people who are impacting. JS: Yeah. ZN: Because so often I think our own perceptions of how the criminal court system works are biased and we can do as much research as we want, but if we’re not in it real conversation with people who are directly impacted, you’re not getting the scoop so to speak, you’re not really getting the real, real. And I know that from standing outside of visiting lines waiting with the mothers and grandmothers of incarcerated young people and hearing from them about their experience what it’s like actually visiting children in youth prisons across the State of California. Look, I remember walking a mile from an abandoned kind of farm area to a youth prison as I’m talking with parents and grandparents, and I’m like why are you all walking, why are you parking a mile when there’s a parking lot at the youth prison. It’s like, well, they check our tags when we come in, and if we don’t have current registration, etc., then they not only won’t they let us visit, but they’ll also call the cops on us. So if you’re not really kind of walking with folks, you’re not understanding the right questions to ask. And that being said, like, there has to be some relationship, right? So you don’t just kind of show up, I think that there’s a need to really connect with community based organizations who have some trust within communities to say, hey, this is what I’m looking at studying, might you be able to help me establish an initial focus group, what are your recommendations in terms of really understanding these issues and really understand that there’s some level of power imbalance in a lot of these situations. And so, you’re doing that work as a researcher in a way that values people’s time, can actually help build the framework necessary for changing these systems over time. And that may not be every researcher’s goal, but from my perspective, the way we see things going today, it’s clear to me that we need a different approach to these issues or I would hope that that researchers would minimally really want to understand the issue well and potentially be a part of that changed process. JS: Yeah, I think that’s great. Well, we have just a couple of minutes left and I wanted to imagine giving you unlimited resources and making you, I guess, attorney general. So you’re head of department of justice and I’m going to give you an unlimited budget. And so, what would your top one or two things, policy changes be, coming out of the work that you’ve been doing? What would those – unlimited budget, so we don’t have to worry about money, what would those one or two top things be to I think embrace some of the things that we’ve been talking about? ZN: I would start by saying like I think the position to really address the crisis that we’re in is the position of governor, like, being able to move the budget as a whole is super important. So people talk about defunding the police, but really at the heart of it, it’s saying refund the community. For [inaudible 00:28:27] 50 years we’ve seen the two things that have been recession proof have been policing and prisons. And as the social safety net has been cut recession after recession, this kind of criminal punishment dragnet has continued to grow, and the power of the vested interest in those systems have also continued to grow. So if I were attorney general, to take their question on more directly, I would make sure that no district attorney, no judges, people couldn’t accept funding from police union because you can’t expect to fully prosecute folks if you’re receiving all kinds of funds from police unions. And so, a first step would be to begin to erode the power of some of these vested interest while expanding the social safety net. And so, right now, in California, there’s a bill called the Crisis Act which was establish a set of first responders to actually deal with different issues that currently police are being sent out to. People who are suicidal don’t need a group of men armed with guns to come to supposedly support them, that’s the opposite of what they need. So really with that power I would want to move resources towards public health approaches to public health issues. And then also find ways to undercut the way in which prison guard unions, police unions have leveraged their increasing budgetary power to shape political power in ways that make them fundamentally unaccountable. And I think that’s not going to be an easy thing, but I think that sort of one-two approach of undercutting the lack of accountability and supporting real public health approaches is the way that we can get out of the situation we’re in now. JS: Right. Really interesting. Zach, thanks so much for coming on the show. I’ll put links to a lot of things we talked about and to the book and I appreciate you taking the time and chatting with me. ZN: Right on, appreciate it, Jon, talk to you soon. JS: Okay. I hope you enjoyed this week’s episode of the show. And I hope the next time that you are working with your data or you are creating a graph or you are writing a report that you think about ways that maybe you can communicate with the people that you are studying or the communities that you are studying, and maybe you can reach out to the people that you’re hoping to communicate with and get their feedback and get their input into your work. So until next time, this has been the PolicyViz podcast, thanks so much for listening. A number of people helped bring you the PolicyViz podcast. Music is provided by the NRIs. Audio editing is provided by Ken Skaggs and each episode is transcribed by Pranesh Mehta. The PolicyViz podcast is hosted by WP Engine and is published on WordPress. If you’d like to help support the podcast, please visit our Patreon page. Credits Music by The NRIs Audio editing by Ken Skaggs Transcription by Jenny Transcription Services PolicyViz.com is hosted by WPEngine and run on WordPress The post Episode #180: Zach Norris appeared first on Policy Viz.
33 minutes | 3 months ago
Episode #179. Kandrea Wade
Welcome to Season 7 of the PolicyViz Podcast! I hope you and your friends and families are well and healthy and safe in these strange and turbulent times. I’ve spent the last few months doing a lot of reading, spending a lot of time outside, and hanging out with my kids and wife. We are gearing up for a new, very different school year where I’ll be working side by side with my kids from our home in Northern Virginia. I’m very excited for this season of the podcast. I have a great lineup of guests coming your way, working in areas of data visualization, algorithms, artificial intelligence, machine learning, and community development. And I’ll be spending time talking with my guests about how these aspects of their work and experiences intersect with race groups, gender groups, wealth and income distributions, and more. You’ll also note some changes to the show. I have new theme music (from The NRIs) and have made some other changes to audio quality and editing. I’ve also changed the show notes page a little bit. To start this season, I invited Kandrea Wade to talk about her research and her work. Kandrea is a PhD student in the Information Science department at CU Boulder focusing on algorithmic identity and the digital surveillance of marginalized groups. Along with developing her research at CU Boulder, Kandrea seeks to discover and assist in creating proper ethical regulations and education on algorithmic identity and digital literacy. With a background of over 15 years in entertainment and media, her interests have evolved from demographic programming for entertainment and media theory to corporate user ethics and legal protections for the digital citizen. Kandrea holds a BA in technical theatre from The University of Texas at Arlington and an MA in media, culture, and communications from New York University. Episode Notes Kandrea on LinkedIn Kandrea on Twitter Scheuerman et al, How We’ve Taught Algorithms to See Identity: Constructing Race and Gender in Image Databases for Facial Analysis iTunes Spotify Stitcher TuneIn Google Podcasts Support the Show This show is completely listener-supported. There are no ads on the show notes page or in the audio. If you would like to financially support the show, please check out my Patreon page, where just for a few bucks a month, you can get a sneak peek at guests, grab stickers, or even a podcast mug. Your support helps me cover audio editing services, transcription services, and more. You can also support the show by sharing it with others and reviewing it on iTunes or your favorite podcast provider. Patreon Transcript Welcome back to the PolicyViz podcast. I’m your host, Jon Schwabish. I hope you and your friends and families are well and healthy and safe in these strange and turbulent times. I’ve spent the last few months doing a lot of reading, spending a lot of time outside, and hanging out a lot with my kids and my wife. We are gearing up for a new, very different looking school year where I’ll be working side by side with my kids from our home in Northern Virginia. But I’m very excited to kick off Season 7 of the podcast and I have a lot of great guests coming your way. You’ll also notice a couple of new things about the show – new intro music, new outro music for one, but the same high quality sound editing. I’ll be providing transcriptions of the show and, of course, great guests. I also have a few new things on the blog. I have a new series called the “On…” series where I write short, sometimes undeveloped thoughts or ideas about data visualization and presentation skills. I’m also entering the final stages of working on my next book, ‘Better Data Visualizations,’ which is set to come out in January 2021. Personally, in response to the many protests about police brutality, and inequality that have been rocking the United States, I’m trying to take a more racial equity lens to my research and to my data visualization work, and I’m trying to extend that perspective to the podcast. So, in that vein, in this new season of the podcasts, you’ll hear more from people of color, and from people doing work to serve underrepresented groups and communities. I’ll also be doing a number of talks in the next few weeks, one for the New York Data Visualization Meetup and another one in October for the IEEE Vis Conference and I’ll put those links on the show notes page if you would like to join me and learn more about the work that I’ve been doing in these areas. So, to kick off this season of the podcast, I am excited to welcome Kandrea Wade to the show. Kandrea is a PhD student in the Information Science Department at CU Boulder. She focuses on algorithmic identity and the digital surveillance of marginalized groups. We talk about what brought her to this area of research, which is a really interesting story, and the different projects that she has underway in her lab at CU Boulder. So, I’m looking forward to bringing you a great set of guests this year and I hope you’ll continue to support the podcast by sharing it with your friends, rating and reviewing it on your favorite podcast provider; and if you’re able to support it financially by going over to my Patreon page. So, let’s start Season 7. Here’s my conversation with Kandrea Wade. Jon Schwabish: Hi Kandrea, thanks so much for coming on the show. How are you? Kandrea Wade: I’m doing very well today, Jon. How are you? Thanks for having me. JS: I’m doing great. I’m really excited to have you on the show and talk about the work that you’re doing. I wanted to start by letting folks know how I found out about you. So, earlier in the year, I signed up my kids for the ‘Skype a Scientist’ program, which is great free endeavor that folks are doing where scientists in all sorts of different fields get up and virtually talk about their work and talk with kids and answer questions. So, I signed my kids up and they attended one on turtles and one on someone doing something with whales or something like that and they were, sort of, moderately engaged. So, I saw yours come up, and I was like, “Ooh, this one looks really good.” I said, “Hey guys, do you want to watch this. This one was going to talk about bias and algorithms and machine learning.” Of course, they just rolled their eyes at me and walked away. But I watched the whole thing and found it really interesting. So, I’m really happy that you were able to chat with me because you’ve got this new paper out. I just want to learn more about the work that you’re doing. So, maybe we can start by just having you talk a little bit about yourself and your background and then we can talk about the work you’re doing. KW: Absolutely! Thank you. Thank you so much. So, about me; I’ve always been in love with art and media and technology which has led me to a super diverse path. So, I have a bachelor’s in technical theater and I worked as a technical director in a lighting designer for over 12 years. In that time, I taught at a college I worked at several concert venues, I held technical director positions. Then, from there, I started working in events and film and TV. I worked for South by Southwest, Viacom, Bravo, Disney, ABC and several other production companies. Over the time, in addition to entertainment, I’ve also worked in education for 15 years. So, I worked as a senior admissions advisor for the Princeton Review. I did that before I went to NYU for my Master’s in Media, Culture and Communications. So, at NYU, I wanted to study demographic programming for production companies, and TV, film, streaming services and as I moved through my program, I realized that in order to do demographic programming, you have to have user data, of course; and this made me start thinking about what exactly these companies can see, and what they’re doing with that user information. So, this introduced me to the whole world of user data ethics and the bigger questions of how we as humans are sorted and categorized in systems. So, I started taking all of my electives and data science and applied statistics and then made my master’s program a combination of media studies focused with data ethics emphasis. I started to focus on bias and ethical dilemmas and the usage of user data, specifically now looking at tech, corporate systems, operations, and government. This led me to CU Boulder, where I’m currently working on my PhD. My focus is in algorithmic identity and the digital surveillance of marginalized groups. I am a part of the Identity Lab, which is led by Jed Brubaker and the Internet Rules Lab where we focus on ethics and policy and that lab is led by Casey Fiesler. So, that’s just a little of an overview of me. JS: Yeah, you have really come to this from far afield. KW: Absolutely, but it’s putting together both sides of my brain that I really love. It’s art and technology, and it’s computers and people. So, it’s really a match made in heaven, perfectly. JS: Well, you seem to have like the practical background to see how the actual production work is done. So, how all this feeds together in that whole ecosystem of advertising and actually producing the media. KW: Absolutely! That’s a lot of it — when it came down to when I was looking at the demographic programming. I was looking at streaming services a lot. The streaming services, kind of, like Netflix being a black box you don’t really know how they’re curating what you’re seeing on your page. Everyone has different sorts that come up whenever you look at your Netflix. So, I found that to be super fascinating. So, in trying to target audiences for what media they wanted to watch, I was like, oh, audience targeting, people targeting, this is all very fascinating to me. So, it’s just kind of trying to understand how people work and what they want and how that’s now being done through computers and algorithms, which is super fascinating to me. JS: Right. Can you talk a little bit about this phrase that you just mentioned algorithmic identity? What does that mean to you? What does that mean in the lab and where you work? KW: Absolutely! So, we all have our physical identity – you have your race, your gender, your cultural identity, your ethnicity, where you come from as a person. But all of that now, especially as we all move forward with these profiles, and all the digital footprints that we create with all the movements that we do online, especially now and the times that we’re in where everyone is on their computer, you now have a digital copy of yourself. That copy of you is not necessarily completely representative of who you are as a person. So, your algorithmic identity is kind of like I would say a proxy of who you are as an actual physical person. So, for every one of us that’s physically walking around in the world, there is a digital copy of us that’s existing inside of these systems; that’s being categorized and looked at sorted all the time. So, in my lab, we look at algorithmic identity and we’re trying to figure out ways to define this, ways to explain this to other people, ways that we need to maybe have protections for what we now call the digital citizen. Because it’s not just about your rights in the physical world anymore, it’s what rights do we have, like, GDPR or ‘To be forgotten’ or what rights do we have to delete our data or what rights do we have to even pull that data if — to understand how many data points there are on us floating out there in the world. So, there aren’t as many regulations and policies and laws as there are for us in the physical world. So, we look at the algorithmic identity as an extension of the self and what we need to be doing to make sure that, that self is protected as well. JS: Right. I want to ask about the paper that you published in May, but maybe I’ll get that through a quick segue question. When you are working on these algorithmic identity measures and profiles and you mentioned earlier distinguishing between different demographic groups in the paper, and specifically you’re talking about race and gender. How does that interplay with how our virtual avatar exists and how companies and governments are using that information differentially across these different racial, ethnic, gender, and other groups? KW: That’s a great question. So, something that happens a lot of times is that as we create these profiles is something that we have is __agency__00:09:34 in them. So, we get to determine, you know, for instance, on Facebook or Snapchat, or whatever your social media profile is, you get to make the determinations and self report your gender, your race, your identity, your age, if you want to, if you don’t want to. A lot of times that’s a really great way that we can actually represent ourselves, but there are also other data points that are being tracked by healthcare companies, insurance companies, credit scoring, and things like that, that we don’t necessarily have as much control over. So, it’s what maybe the government defines us as and what markers we have to take on boxes for census purposes or birth certificates. Those can be literal boxes that we’re put into that we have to define ourselves. Those little data points get put into systems where there are basically assumptions made about us or there are matching that’s done for certain profiles. So, there are different implications that come from that. What we see a lot in this field is that typically the same, sort of, socio economic, really real life physical bias that we see and some of the discrimination that happens in the real physical world is now unfortunately being transferred into these digital systems. So, the implications that we see a lot of times happen to be really affecting, you know, like I study marginalized groups, they really affect people of color, LGBTQIA+ groups, people with disabilities. They are either not being considered or due to bad historical data that’s been collected on them or biased historical data that’s been collected we’re training the systems in a digital space now to reflect the same bias and discrimination that happens in the physical world. So, that’s a lot of what we look at and how we can maybe mitigate and solve a lot of that because computer systems can do what humans can but faster and at a bigger scale. So, we want to make sure that if we’re going to be categorizing humans in this way in systems now that we can account for the bias that’s being input into those systems. JS: Right. So, I want to ask about the data collection part, but I want to give you a chance to talk about this paper that you’ve published, because it goes hand in hand with what you just said. So, the title of this paper is, and I’ll post it to the show notes for folks who are interested, ‘How We’ve Taught Algorithms to See Identity, Constructing Race and Gender and Image Databases for Facial Analysis.’ So, I thought maybe we could start by having you just give us the overview of the paper in general terms and then we can dive into some of these details. I have a few questions for you about the paper itself. Yeah, maybe just give us an overview of what the hypothesis is. Then, also maybe talk a little bit about how the data are collected in both this paper and just more generally. I mean, I certainly don’t have a background in this, so I’m just curious how the analysis and the data are actually collected and used. KW: Absolutely! So, this was a study of 92 image databases that are utilized as training data for facial recognition systems. So, we wanted to analyze how they expressed gender and race and how those decisions were made and annotated. I mean, if they even were annotated, and how diverse and robust those data, image databases are. So, our findings showed that there are actually issues with image databases as to be expected, but those issues specifically surround the lack of explanation and annotation when it comes to how race and gender is defined within those databases. So, we often found that a) gender was only represented as a binary. So, that is as in male/female, except for a few instances that accounted for it in their reporting, but still only contained images that were listed in the binary. Then b) we came across issues of race either being defined as something insignificant, or indisputable, or apolitical when we know that in the physical world there are many layers of sociopolitical factors like status, income, country of origin, parental lineage; you know, all of these things play into how someone’s race or ethnicity is defined. We also noted that the diversity of these databases was often lacking. So that, again, contributes to the problems that we see so often in facial recognition systems and their ability to recognize diverse faces in especially those of color in individuals of trans identity. JS: So, when the facial databases have this information, I assume it’s being collected in multiple ways. So, one is I as the individual, there’s a picture of myself on Facebook and I can tag myself, gender, race, but whatever, Facebook, whatever tool options they give me. Then that informs how an algorithm might assign those characteristics to that image as well; is that correct? KW: Yes, that’s correct. In this paper, in particular, we were looking at databases that had already been built for public use, for corporate use, and for things like that. So, these had already been built, specifically, let’s say from a lab that recruited people, they wanted to build a database, they wanted to give it to people, sell it to people, but they just went ahead and put out a call for faces and then they decided to collect those images and then they categorized them. So, a lot of these databases were already pre-built as a package to be given or shared with the world to train their own systems on images. JS: Interesting! I want to get more into the content of the paper. I do want to start with the title, because I think that title is actually telling with how the language in the rest of the paper. So, the first part of the title of the paper is ‘How We’ve Taught Algorithms to See Identity,’ and I was wondering if you could talk both about the connotation of that; of how it’s not just algorithms don’t just exist, like someone has to build them and train them. Also, how you and your coauthors and also, I guess, the folks in your lab view how these algorithms are constructed in terms of how they reinforce the stereotypes, the discrimination, the racism, and prejudice that you’ve already mentioned, but yeah, I guess just that overall sense of how the title here and the language throughout the paper is more, I don’t know, it’s certainly active, but it’s also takes responsibility for these algorithms as opposed to just saying, “Yeah, they just kind of exist and off they go.” KW: Yes. So, I would say that the use of this language could be considered how we’ve taught — is maybe we could say it is the royal we, which accounts for all of us in the field – so that researchers, practitioners, coders, even the participants who provide the images that go into these databases. We’re all responsible for teaching systems of AI and machine learning how to do the jobs that we’re asking them to complete. So, it’s up to us to do a better job of ensuring that those systems are fair and equitable for all races, cultures, or gender identities. These systems are really no smarter than a toddler, essentially, and will never do anything more than what they’re told with the information that they’re given. So, when it comes to machine learning, well, that machine needs to be taught. Like I said, it’s up to us as those teachers to give those algorithms their best shot at being as accurate and equitable, fair and representative as possible of all of the people that it’s trying to assess. JS: There’s a lot of talk in the data and data visualization field about being responsible consumers and users of data. I’m curious if you’ve thought about how consumers of, I guess, this information, which is sort of a weird things. So, I think about a lot of the media that we’ve talked about earlier, it’s — my ads on Gmail and Google are being targeted towards me, but what can we do as consumers of information to, I guess, try to be a responsible consumer of this information, maybe it’s the easiest way to say it. KW: So, and it being just to every person who’s using a computer or things like that. It depends on if you want to be identified or not. I mean, there are two different ways to look at this. It depends on if you want to be identified and if you do want to be identified, ensuring that it’s accurate and so from what I have gathered generally in my research, a lot of people are very uncomfortable with being identified especially in marginalized or communities or protected classes, groups and others that may be at risk of surveillance essentially. So, what those people do is a lot of people spend time obfuscating their identity. They would rather not be found in a system whatsoever and if they are found they don’t want that to be accurate. Now, a lot of people don’t know that there’s a backend feature to Google, and I think you can do it in Facebook as well, where you can go in and see what they think of you for marketing and ads. So, you can go in and see what they think your political leaning is, what they have assessed your race to be if you’ve never even entered that information. You can go look this up and a lot of people would rather that information be inaccurate because they don’t want ads targeted to them. They don’t want an online system that can make a determination about credit scores or things like that. They don’t want that to find the information about them. But if you do want to be found online which is completely reasonable to or if you do want to leave this digital footprint, the most you can do as a consumer is just ensure that it’s accurate. So, there are ways that you can go into your own profiles and edit your information to make sure that it is as in line as possible with who you are as a physical person. But also, go into the backend of Google, see who they think you are and then you can either change the settings in there manually, or you can change your user behavior to be more in line with who you are as a person. It all just depends on how much you want to be involved in this digital space and that’s up to every individual to make that determination for themselves. JS: Right. Before I ask a little bit more about the paper, I wanted to turn back to something you just mentioned, because I want to make it clear for folks who may not be thinking about this, that what we’re talking about here is not just you’re scrolling through your newsfeed and there’s an ad for the thing you just looked at on Amazon and just shows up in your app. It’s not just about advertising. I was just wondering because you had just mentioned credit scores, which I think is a great example of credit scores, health, and housing. I was as just wondering if you could talk about a few of the things where these algorithms can reinforce these stereotypes and discriminations that you’ve already been talking about. KW: Absolutely! So, I’ll talk about credit scores and I’ll talk about when it comes to insurance and maybe loan determinations. So, there’s a lot of different data points that are used; like we’ve been talking about, but one of them — and they’re all proxies of who you are as a person. It’s they don’t know exactly who you are, so they have to use other data points to make assumptions. Again, a problem; anytime we make assumptions that’s not a good thing. But zip code. Zip code is one that is used really widely to make determinations about who you are as a person and this goes into bias. This is how we have gerrymandering. We have a lot of lines that are drawn that separate people from different resources, school systems, hospitals; that are also put into these algorithms that make determinations about if you are worthy or deemed worthy of receiving something like a loan or deemed worthy of receiving a certain type of health care or at a certain rate. So, these things are determined by where they think you live and what type of neighborhood that is or any person can decide to buy a house in any neighborhood, but they make these assumptions based off of what they typically and historically have seen of those neighborhoods, whether it be a more disadvantaged neighborhood or they see it as being very wealthy and lucrative neighborhood, something as simple as your zip code to make determinations about how worthy they deem you to be. When it comes to things like health care, there are different issues that come into play with the reporting that doctors have done. When doctors have in the physical world been — there’s been a lot of discrimination and people not recognizing the symptoms may be different in African-Americans or in black people versus symptoms that represent, let’s say, for heart attack in white people. So, those same biases that were written into charts get input into these algorithms. So, there are misdiagnoses that happen even with the assistance of AI, based off of there not being fair reporting on what these symptoms are looking like and who deserves to have treatment for them or not. Then you have, things like — like I was saying, you have finances that are being tracked as you make your purchases online. It’s not just about the ads that you see, it’s about these algorithms also being able to see what you’re buying, when you’re buying them, how much money you have in your bank account, and how much credit you have. With all of those things being put together, it’s making a profile on who you are as a buyer or as a consumer. So, that’s also leading into determinations about what you may be deemed worthy to receive or not when it comes to making requests or what ads you see or making requests for loans or even purchases that you have. So, those are all things that are being tracked and systems all the time. They’re not just trying to target you to sell you things. They’re also trying to see what you’re buying to make determinations about who you are as a person and where you are buying these things. Are you shopping at Wal-Mart, are you shopping at Nordstrom, are you shopping at Barneys New York? Those are all very different things. JS: Right. Yeah, I think Virginia Eubanks has this great book on this topic and I think she has described this as the virtual redlining of our society where we’ve moved now from these physical maps of housing discrimination into the virtual world, which as you mentioned, works a lot faster and a lot broader because we have computers doing it all or computers are doing it now informed by the decisions that people are making when they build the algorithms. KW: Absolutely! JS: I wanted to ask one last question about the paper. So, there’s a sentence in the paper, for me, really struck me because I focus a lot on good annotation and data vis and I thought this was really interesting. So, in the paper, you and your coauthors say, “…further when they are all annotated with race and gender information, database authors rarely describe the process of annotation.” I was hoping you could talk a little bit about what annotation means in the context of your field and your research. KW: Absolutely! So, in this particular study, we’re looking at images, right? So, when it comes to writing a description of an image of a person, and giving that description to a computer, those algorithms need information like race and gender, or at least in the systems as they’re built of this moment, they need markers like race and gender to be able to start to sort, categorize, and match similar images. Well, those descriptions are written by the people who are developing those databases of images and we often found that this process was done in a very vague but determinant way. So, when the individuals who collected these images saw what they presumed to be a black person, they labeled the images black; when they saw who they thought to be, white, Asian, Indian, male, female, they made these determinations for the subject in the image, and typically made this with no clear distinctions or justifications for why and how these assignments were made, aside from, ‘well, we just did it.’ So, these distinctions and justifications would be the annotations. So, it would be some, sort of, previously defined set of rules or guidelines that would inform exactly why the images were labeled as they were. There would need to be guidelines for what is defined as a woman visually, what is defined as a man visually, what is determined as to be black and white, and so on. But without these clearly defined rules and guidelines, which could then be argued, disputed, iterated, or improved upon we’re just left with determinations on images of individuals that may not be accurate or true to what they would see as their own identity. Then there’s no way to then argue them or refine them to be better and more accurate for these people. So, it’s basically just like, someone said so, and that’s why that’s not a real annotation or justification. It’s just because, ‘I said so.’ One of the main issues with databases attempting to determine identity is that the identity of the subject is often reported for the subject, instead of allowing the subject to self identify. Then again, those in charge of creating the databases have now essentially defined race and gender for an entire set of individuals without considering their lived embodied experiences or their positionality. So, in the paper, we talk about this as the visible versus invisible features of identity. So, think about it; if you don’t have a diverse group of people may these determinations, and they are only using categorizations that they defined and do not explain, it’s really easy to see how this leads to contextual collapse. It removes a lot of potential variance in diversity that we could have in these databases. Not to mention that, again, the individuals who are typically pulled for these images are not representative of many diverse populations that we have in the world, and they often skew toward being white or white appearing people. So, if we take all of that into account, and then we teach it to a system, and we tell it to read a face, and let’s say it’s a black, Native American trans person’s face, the system already cannot recognize it as a trans person due to it only being capable of reading gender in the binary, male/female. It also now has trouble reading a black face due to a lack of training images in the database, and then it’s lost in finding distinctions between black and Native American because it was never told that Native American was something to look for. So, now we have a system that can’t identify a person that it’s attempting to read and if it does, it will output incorrect information just doing the best with what it’s been told. So, this leads to many issues in facial recognition that have really serious implications. Like, right now we have a lot of issues of misidentification with things like traffic cams, and street surveillance being used, especially right now during the protests. There are a lot of issues of a hard time with identity verification, especially for diverse people when it comes to like passports and IDs at airports. They use a lot of facial recognition for that, and it slows down the process of diverse groups of people being able to even get through security to get to another country and systems just not identifying diverse subjects at all, and giving back feedback or error responses. So, there’s an entire world of issues that plays into this, but that’s essentially what we’re looking at in the paper and that’s where we were looking at annotation, with the direction _____00:29:00 requirement there. JS: Really interesting. Well, I’ll post the paper or a link to the paper on the show notes. Before we go, I wanted to ask what work you have on the horizon. What’s the future bring for you in terms of your work and your dissertation and the lab? KW: Oh, thank you for asking. Yeah, so I’m doing some work. I am right now looking at how qualitative researchers conduct their work. We’re trying to find some ways that maybe we can have a better understanding of how qualitative researchers conduct their work in their analysis. If there are ways that we can maybe potentially build tools to be able to help them to be able to do these things. I’m also looking at some issues of where in the country do we see the most lacking of data literacy and how do we help those communities to be able to inform themselves, educate themselves on how to be smart consumers of data like we were just talking. I am also looking at, right now, the things that are going on with the protest and protest surveillance, and moving forward looking at a dissertation or work later down the road. Like I said, I have a background in media and entertainment and I really have a love for theater arts because I feel that it has a way to connect with people in a way that a lot of other things can’t and so entertainment is extremely powerful in being able to disseminate messages. So, data literacy is a very huge subject to try to teach to people and so I do see, down the road, that I’d like to incorporate data literacy into messages of passive learning via entertainment, theater arts, and so trying to make that – it’s a bigger goal but I’ll refine it as we go; but those are really what’s on the horizon for me. JS: That’s great. That sounds like great stuff and sorely needed and I think you have a lot of work to do. KW: Well, thank you. JS: It sounds like great work. KW: Thank you so much. JS: Kandrea, thanks for coming on the show. This was really interesting. It was great to chat with you and I really appreciate it. KW: Absolutely! Thank you for having me. Thanks to everyone for tuning in to this week’s episode of the PolicyViz podcast. I hope you enjoyed that interview with Kandrea Wade. I hope you’ll check out the various links and resources that I have put up on the show notes page. You can go check out Kandrea’s bio, you can check out her research and her work over at her lab at CU Boulder, and you can check out the various talks that I’ll be giving over the next couple of weeks. So, until next time, this has been the PolicyViz podcast. Thanks so much for listening. A number of people go into helping making the PolicyViz podcast what it is. Music is provided by The NRIs, audio editing is provided by Ken Skaggs, transcription services are provided by Pranesh Mehta, and the show and website are hosted on WP Engine. If you’d like to support the podcast and PolicyViz, please head over to our Patreon page where for just a couple of dollars a month, you can help support all of the elements that are needed to bring this podcast to you. The post Episode #179. Kandrea Wade appeared first on Policy Viz.
39 minutes | 5 months ago
Episode #178: Valentina D’Efilippo
Valentina D’Efilippo is an award-winning designer, creative director, and author based in London. Working across formats and industries, her work takes many forms – from theatre productions and exhibitions to editorial content and digital experiences. She has co-authored “The Infographic History of the World”, and illustrated “The Brain: A user’s Guide”. Her work has been exhibited internationally and her dataviz “Poppy Field” has become part of the permanent collection of one the largest anthropological museums in the world, The Weltmuseum Wien.She also leads workshops attended by students and professionals, including a series of Masterclasses with The Guardian. This is the last episode of the podcast until the fall. Please be on the lookout for more great episodes starting again in September. In the meantime, enjoy your summer, catch up on some rest, some reading, and, of course, old podcasts! Episode Notes Valentina on Twitter Valentina’s website & portfolio Poppy Fields project MeToomentum project Book: The Infographic History of the World Blog post: Sketching the World: An Icebreaker to Working With Data DataViz & Infographic Storytelling workshop Support the Show This show is completely listener-supported. There are no ads on the show notes page or in the audio. If you would like to financially support the show, please check out my Patreon page, where just for a few bucks a month, you can get a sneak peek at guests, grab stickers, or even a podcast mug. Your support helps me cover audio editing services, transcription services, and more. You can also support the show by sharing it with others and reviewing it on iTunes or your favorite podcast provider. Transcript Welcome back to the PolicyViz podcast. I’m your host Jon Schwabish. I hope you and your friends and your family are all safe and well and healthy in these strange times. This my friends is the final episode of the PolicyViz podcast for the season. No, don’t worry, I will be back in the fall with more great interviews, with folks in the fields of data visualization and open data and presentation skills and technology, but I’m going to take the next few months off and rest and relax and recharge. And to finish off this season of the podcast, I’m excited to have Valentina D’Efilippo with me. Valentina is an illustrator, a designer, a teacher, and a writer, and we talk about all the sorts of things that she does keeping her busy. When you look through Valentina’s work, you don’t see a lot of line charts and bar charts and pie charts what you might consider some of your standard chart types, but instead she spends a lot of times creating new and different forms and non-standard graphs, and so we spend a lot of time talking about how she thinks about communicating data in those different ways to her audiences. We also talk about her data visualization infographics workshops, one of which is coming up very soon. We talk about some of her mapping exercises and some that I’ve actually used in my classes when I teach to kids. So I hope you’ll enjoy that conversation with Valentina, and I hope you’ll continue to support the podcast by sharing it with your friends and your social networks, I hope you’ll consider leaving a review or rating on iTunes or Stitcher or Spotify or wherever you listen to your podcasts. Before I get to the interview with Valentina, just a couple of things as I think about the next few months and reflect on the few months behind us. It’s obviously been a very strange and difficult and challenging few months both with the COVID pandemic and here in the United States and around the world, the protests against police brutality and inequality. And as I think about my work in the field of data visualization or presentation skills, I’ve been starting to think more carefully about accessibility and diversity and inclusion and equity and how we can do a better job of communicating our data and our analysis to more people so that they can use it, so that they can make discoveries and they can improve policy in the world around us. And so I’m excited to continue that journey with you as we continue to think about ways in which we can make our work better and more accessible and more relevant to the world around us. Part of what I’ll be doing over the next couple of months is finishing up my next book Better Data Visualizations, which will hopefully walk you through many of the different types of graphs that are available to you outside of these lines and bars and pie charts, and that’s why I’m excited to talk to Valentina because she creates a lot of those non-standard graph types. So again, I hope you’re well and I hope your friends and your family are well and I hope you’re staying safe, and so I’m excited to bring this final episode of the season’s podcast to you. Here’s my interview with Valentina D’Efilippo. Jon Schwabish: Hi Valentina. How are you? Valentina D’Efilippo: I am great. Thanks for having me. JS: Of course. I’m excited to have you on. It’s the last episode of the podcast for this “season”. So going out with a bang because I get to chat with you and what couldn’t be better than that! VD: Amazing. I’m delighted. Thanks. JS: There are a few things I want to talk to you about, but maybe we can start with you telling folks a little bit about yourself and your background and what you are doing now, and some of the work that you do. VD: Sure. Okay, so let’s start with labels. I’m a designer, illustrator, and creative director. I’m Italian, as you can probably guess from my accent, but I’m based in London. And I guess, I’ve been working with data for more than a decade now. But yeah, very different formats and different industries. So, I guess, when you’re looking at my portfolio, you would see, like, many different ways of representing or perhaps working with data. Sometimes, it’s pretty standard, like interactive platform or editorial Commission’s. Other times, it’s a bit more unusual perhaps. So I’ve been working with theatre productions where we talked about privacy or climate change during live theatre performances or exhibitions or digital products where perhaps we don’t even visualize data but we use data as a way to create an experience. And, I guess, the common denominator of all these projects is data or working with some sort of complexity, I suppose. So yeah, of all the labels, I suppose information designers in the designer is the one that fits the bill, yeah. JS: I find that when I do this podcast, people come to the field of data visualization or information visualization from all different ways, you know, there’s no like single path. So you are a designer by training and by background, how did you get into this, especially because you started doing the data part 10 years ago, so even, especially back then, it wasn’t sort of a standard path, so how did you end up going down this path of working with data and combining it with your design training? VD: Yeah, interesting. So it’s been a journey. So I graduated industrial design at Polytechnic of Turin, and then I came to London and I did a Master’s in Visual Communication. And, I guess, like, from the beginning, my first steps in [inaudible 00:05:43] design were really just experimentation with topics and subject matter that I was interested in, and because of the background that I had there was quite analytical and perhaps more engineering, I was deconstructing everything, I had to put my hands on while I studied visual communication and graphic design. So for instance, at the beginning I did the construction of the [inaudible 00:06:05]. My thesis in my postgraduate was visual analysis of stereotypes and how those gender stereotypes specifically are portrayed in the literature for kids. So I did a recollection of many symbols and colors and activities and emotions in which females and males were portrayed. And unfortunately, this was like 2005 and 2006, it was pre [inaudible 00:06:36] the amount of bad stereotypes in which men and women specifically were described in this literature. And yeah, as you were saying, like, probably at that time there wasn’t really a thing called data visualization. Those projects started to fall into the bucket of information design in my program and later on I kind of understood that, yeah, since the beginning, my first step into visual communication were always kind of like driven towards visualizing complexity, making sense of complexity, breaking it down and then piecing it back together to explain my day inside, so what I learned to others. And then really the project that cemented my practice came years later. So the first job that I landed after university was actually in advertising, in digital advertising. So I worked as an art director for a number of years. And then in 2012, I got an email from HarperCollins, which is obviously a big publisher, and they got in touch saying, we saw your experimentation with data, like those projects that I just mentioned, visualizing gender stereotypes, and we would like to discuss with you that you’re putting together a book about the history of the world through data and infographic storytelling. And I was like, oh my god, this is amazing. But at the same time it was like, really, do have the skillset to do that. But anyhow, it was an amazing opportunity. It was an arranged marriage as you would say. So I was paired with the brilliant James Ball who at the time used to work for The Guardian as a data journalist. And together, yeah, we put our brains together, and we created a 100 infographics from scratch, narrating the evolution of the world and the evolution of mankind and this is the Infographic History of the World, the book that came out in 2013. Yeah, I guess, it was like a very ambitious project, very hard, certainly a daunting brief, but absolutely amazing, an amazing opportunity – an amazing learning experience more than anything is by doing that you actually learn how to do it. JS: So when you finished the book, is that when you decided just to start doing information visualization and teaching and workshops full time? VD: Yeah, I guess, again, it wasn’t quite a decision, it was just, I guess, one thing just led to another thing. So the book came out and there was the business card, it just opened a lot of opportunities. So I started to receive more and more briefs and commissions that were labeled as infographic projects or DataViz projects. Then the Guardian got in touch saying we’re putting together these master classes and we would like to expand our curriculum and include infographic storytelling, would you like to teach. So one thing kind of led to the next thing, it’s life. JS: As it were, as in life, yeah. I want to talk about the workshops in a minute, but I also wanted to talk about your, I guess, that’s your style or your approach to data visualization, because when I scroll through your portfolio, I don’t see – I mean, obviously, I see lots of different types of work as you already mentioned, but I also don’t see a lot of line charts and bar charts and pie charts and area charts. There’s a lot of – you have a lot of complex data and there’s a lot of different forms going on with your work, and so I’m just curious about that aspect of your work. Are you anti-bar chart or anti-line chart or is it more that creative side of the brain, sort of takes over – so I don’t really know how to formulate a precise question but it’s more of an observation I think I’ve made about your work over the last couple of years. VD: Yeah. No, I guess, like in the portfolio, there is a clear curation of the type of work that I would like to work on. JS: Yeah, you are right. VD: So there is a filter that I apply, I clearly do lots of bar charts and conventional charts in the day to day but I suppose, like, whenever I can, I try to push it, I try to kind of like find a way to balance form and function, obviously depending on the audience and brief, the kind of like purpose of the visualization. I try to combine the informative aspects of what we’re trying to do when we’re creating a data visualization, like, a simple bar chart to the more creative aspects as you were just saying to create something that perhaps is more compelling, maybe more aesthetically pleasing or they can perhaps resonate more with an audience from a semiotic point of view. What I mean is how can we actually bring the narrative behind the data behind these numbers to life through the use of color, use of visual metaphors, novel forms, different aesthetics that perhaps we borrow from other fields. I guess, like, a lot of times we tend to think that a chart is like a standard chart, like a bar chart, is easily understood because anybody can read a bar chart. But is it true? Can everybody read a bar chart? And also are we really bringing to life the stories of these bar charts by representing bananas, let’s say, in number of deaths in the same conventional way? So, I guess, those are the questions that I keep posing whenever I’m approaching a new brief, and sometimes a bar chart is the most appropriate way to go about, other times I might just experiment with something else. JS: And do you find, when you have these experiments and you end up on a form that you like, but it’s not a bar chart, it’s not a line chart, it’s something different, do you find that you have to spend – well, actually, I was going to ask you about how you spend time explaining to the reader or the user how to read the graph, but actually I want to back up – how do you explain to the client that this is maybe a better, you know, maybe they’re coming to you and saying, oh I’m – maybe they’re expecting a dashboard or maybe they’re coming to you because they don’t want a standard dashboard – but do you have to explain to them why this form that looks, you know, doesn’t look like anything they’ve seen before, it’s actually a way that they should go, this is a better way? VD: I guess, as I was saying before, it really depends on the brief. So depending on the audience and the type of communication, the type of design work we’re doing, I might need to just stick to whatever is conventional. So let’s say, I’m designing a trader platform. It’s not that I’m going to be redesigning the way that the trader does the work. And that will leverage the way the visual cortex has been trained for years and years. So I’m not going to enforce like a new novel way of creating the dashboard. On the other hand though, whenever I’ve got a brief that allows me to be more creative, I suppose, the reason they’re much selling work to the client to kind of like convince them that there are different ways, because usually it’s kind of like a process, we go from the insights, the data, what it’s telling us, discussing the stories that those insights communicate, and then it’s a journey to getting into the forms and the shapes and how those can be communicated. So as long as you kind of like always reference the numbers in the stories, then the forms actually comes, yeah, it’s a normal evolution, I suppose. It’s not something that you are trying to inform, let’s say – oh I really want to do – I am saying something stupid but, like, I really want to do a flower, and then you actually look at the data, it doesn’t make any sense to create a flower out of this data, because the flower doesn’t connect to the story, and it’s probably not the most appropriate way to represent the data shapes either in terms of pre-attentive processing. But if you’re looking at the data and you look at the story and then you can find a connection with a flower, then why not? Does it make sense? JS: It does make sense. It’s really interesting how, especially your comment about including the data on there somehow makes it, or not somehow, but it makes it easier for people to read and understand it, because they can read the numbers right there. VD: Yeah. And, I guess, like, now that I said, I talked about this metaphor of the flower, I guess, I can briefly just discuss these poppy fields perhaps is one of the most popular visualization, it’s simply a scatterplot. So we’re looking at numbers, and the stories. What we’re looking at is the last century’s war from the 1900s until the present day where the war took place and the toll in terms of the cost, the human cost, the number of lives that each war claimed as well as I think we had the geography but quite broadly speaking just a continent, so the starting point is obviously the data, we’re looking at the data and what type of shape is best suited to actually represent all these different variable, because we’ve got a magnitude, number of deaths, we’ve got time, so some sort of timeline, and we’ve got geographies to kind of like [inaudible 00:17:02] these wars. I guess, after short exploration, scatterplot seems like evident to be like a good way to go, where perhaps we’ve got a bubble chart on a scatterplot. And the bubbles are representing the number of deaths that each war claimed, and then in terms of timeline where do you place these bubbles, at the beginning, at the end. Seems intuitive to put it at the end, because that’s when you count the number of deaths, right, the end of the war is when you actually see the cost of the war. So you [inaudible 00:17:36] the bubble there. And then what do you put on the Y axis? On the X we’ve got time, on the Y I’m thinking duration to see how long the war was, because on the timeline I just have the end points. So I want to see when it started, when it ends, so I get duration on the Y axis. So this is kind of like initial exploration, I see what the numbers are telling me; and by visualizing just in this simple bubble chart and a scatterplot, I can see some interesting outliers, like two big bubbles at the beginning of the century the Great War, and then in the middle of the century the Second World War. And then I see right at the end of the timeline, a small bubble but really tall in terms of the wide positioning [inaudible 00:18:28] it’s been lasting for six decades, Palestinian – Israel and Palestine. And that’s pretty much the first investigation. Now that I’ve got like an idea what the skeleton of the visualization looks like, I’m kind of thinking, okay, now instead of just babbles, what can I do. And that’s when I come up with the idea of, like, it can be a poppy field, it can be like a field commemoration. So what if I dress those bubble with flower. And then if you think about the flower, then I’ve got a new element that is actually the stem, so I can anchor the flower to the timeline in the moment where the war started, and then make it grow horizontally as well as vertically to indicate the passing of time. So I’ve got timeline both for the duration vertically as well as the duration horizontally on the time on the X axis. And yeah, those poppies can change slightly variation of rate to kind of group them together in terms of geography, and that’s basically the process of getting from something that is quite standard like a scatterplot into something that is perhaps a bit more novel that is a pop field. JS: Right. It’s interesting the way you describe it as going, in some way, step by step and just letting the data inform how you evolve the form of the piece. VD: Yeah, absolutely. I think the starting point is always the data, I always need to see the numbers and what they look like, and that usually happens in a very raw way in Excel or Google spreadsheets, sometimes in RAWGraphs, sometimes in Tableau, but I just need to see the numbers and the kind of insights. And then I apply all the visual communication, the graphic design, semiotics later on. I guess, it was only one case where the visual metaphor actually unlocked the data puzzle, it was in MeToomentum – it’s funny that I’m referencing to projects that both use flower as a metaphor. So not all my projects are flowers. But anytime the starting point is always the data, and then I go into visualizing the data and then get into the visual metaphor. But in this specific case, for MeToomentum, that is a visual analysis of the Me Too movement of the first six months, I really got stuck at the beginning, like, with a million data points, we’ve got so many tweets related to the Me Too hashtag, and we had a very multidimensional dataset, we have the geography, we had obviously the time in which the tweet was shared, the person who shared it, the number of followers, also the number of likes, the number of comments, and obviously the [inaudible 00:21:40] data that is contained in the content of the tweet. So doing some sort of semantic analysis you could find meanings and frequency of words and all of that. And that was like, wow, okay, where do we start to piece it all together. And more than anything, it wasn’t even just the complexity of the data, it was actually the complexity of the subject matter and looking through the datasets was really hardcore, reading through these tweets and the stories was like really challenging, really, really hard. At that point I actually felt a kind of heaviness of working with the data and irresponsibility as well of like, am I going to paint anything meaningful, how can I actually do justice to these voices. And then kind of moving forward into the direction of like painting and thinking, okay, it’s just going to be an expression of what this dataset, it is actually just a drop in the ocean, because obviously the movement has been massive, and we had all the limitations that come with scraping the Twitter API and so forth, thinking, well, what if I just paint an image, what could this image be. And I thought, what are these voices, these voices are amazingly powerful, but on their own, until now, they’ve been incredibly fragile. And that kind of brought me to think about the dandelion as a visual metaphor for something that is regarded as something beautiful as well as fragile like a female voice, like, you just blow it and it disappears, but it’s also amazingly strong because dandelion is actually not a flower, it’s a weed, like, you blow it and all these seeds can just grow anywhere, and it’s really fertile, it can grow pretty much everywhere. So you like to see in field, but you don’t want to have it in your garden, kind of thing. And also if you think about the dandelion is this symbol that is used in popular culture, many times symbolizes hope or the hope for change when you blow at you, say, most of the time, did you make a wish. So they are kind of like semiotics dictated and the shape of the data analysis. So in that case, in this specific project, I was like, okay, what if I could paint this data with a dandelion, what attributes do I have in the form of the dandelion. So I’ve got the seeds, they could have different length, they could have different size, and then I started to kind of plot the data into the visual metaphor if it makes sense. But there was the only case where I actually went reversed. JS: But it sounds like you had this connection with the data in such a way that the form sort of informed how you were going to do the work. VD: Yeah. JS: That’s interesting. It’s also interesting the way you describe your process. The way you describe, it’s sort of very flowing from one state to the next. So it’s not so much like I did this, and then I did this, and then I did this. It has this – the way you describe it just a little bit more, has more of a flow to it. And I’m curious, so I know you teach a lot of workshops, you mentioned the Guardian Masterclass – is that how you teach people to create information visualizations, like, again, I don’t have a specific question, but what is your approach to teaching this skill which, as we’ve already talked about, people come from all different ways to be creative with data? VD: Yeah, so that’s an interesting challenge, the workshops. So I run workshop with the Guardian graphic and this is an organization in the Netherlands as well as corporate trainings or workshops with university students. So I’ve got many different audiences and also the length of my workshop can vary from three-hour format to a week or perhaps a couple of weeks if I’m working with university students. And I started actually with the Guardian, that was my first commission I would say of infographics storytelling workshop. And, I guess, I took the challenge as a design challenge – how can I actually explain to other people what I do, how can I design a format that will explain that. And, I guess, as you were saying, it’s kind of like sharing with others this flaw, how do you go from the raw spreadsheet into something that is visually compelling as well as highly informative. So I created a few activities and exercises that hopefully illuminate the process, and it’s very analog. I try to keep all these workshops very tool agnostic to kind of remove the barrier of tack, and also because, let’s say, at The Guardian I might have a group of 20 people and all these 20 people are coming from different backgrounds, like, some might be data scientists who are very fluent with data in a spreadsheet, but other could be storytellers or health practitioners or even just students or retired people, they just want to kind of expand their knowledge and become better consumers of charts and data. So I tried to remove a few entry points, like, the tooling and really just focusing on kind of like decision making process that goes into creating successful database. And yeah, I guess, like the aim of this format is always like to create something that is interesting, informative, inspiring, but also highly accessible. So for anybody to be able to create something, I want to everybody be able to participate. And I do create a number of activities with just pen and paper. So you are kind of familiar with the first activity, I suppose, because I spoke at information class at the conference where we met about these activities, that is mapping the world geography from memory, and then on top of that, we’re going to be mapping personal dataset. So perhaps I can talk a bit about that. These initial activities actually are an icebreaker in my formats and it’s based on an obsession of mine. So I’m a map collector, and I’ve been collecting hundreds of maps for about 11 years since 2009, when, for the first time, I visited Japan, and I saw a presentation of the world where Europe was not in the middle, and I kind of felt lost. I was like, oh my god, what’s going on, east and west are reverse, the Americas in on the wrong side. I kind of felt disoriented. So I turned to the local people and asked them to draw the world map from memory to just kind of like sketch it really, really quickly for me. And over the 15 maps that I collected, all of them presented Japan in the middle, and the geography around was somehow more detailed. And then the rest of the world was very much personal, was very much subjective to each one unique map. And that’s kind of like a fascinating thing for me, like, how we are innately able to describe a concept like the world visually, but at the same time it’s like so unique to each individual person, based on our own experience, our perhaps knowledge of geography as well as ability to draw. So I’ve been doing that for a number of years, and then when I started to design the format of my workshop, I thought, wouldn’t it be cool to actually introduce people by drawing their own world map, because then we could see where people are from, where perhaps they’ve been. And then on top of that I thought what if then I can use that to actually plot some data. So the map itself is already a representation of information and personal data that I can walk people through, that we can share. And then on top of that, we can then map a dataset, a specific story. So to be a bit more specific with that, after we draw the world map, we think about a story that could be, maybe the trips that you’ve taken; or if you haven’t traveled extensively across the world, you might think about the food that you consume, whether it’s Japanese sushi or Chinese takeaway or Italian pizza and spaghetti or, I don’t know, Mexican tacos, anything – anything can make the story. The only, I suppose, filter of all the personal stories that you can possibly introduce yourself to the class, is it in needs to be global because we’ve started with a world map. And [inaudible 00:31:24] we started with a world map, and I think it’s quite effective in a way, because the blank canvas can be very intimidating, especially, if you’re not coming with a creative background, if you’re not used to drawing and sketching, staring at the blank canvas and thinking like, oh, now I’m going to draw this dataset, it can be like incredibly challenging and intimidating, especially when you’re in a group with strangers. So anybody can somehow articulate what the world looks like when you’re asked to do so. So it’s a nice kind of like icebreaker in the sense that everybody can actually start noting down something on paper. And then the next step is to plot this specific story that you might have chosen, like, the travels that you made or the food that you like or where your family and friends are from, whatever the story might be. And usually, in live events, so if it’s like face to face workshop, because nowadays obviously everything is online, so the sophistication of paper choices is not available. But if it was a live event, I would bring tracing paper, and I love working with tracing paper on my own work because then you don’t need to start from scratch over and over, you can just overlay a new layer on top of the map and start with a new dataset, perhaps for correlation to see two different datasets. Or if you perhaps were not happy with encoding that you’ve just done, you can just remove the tracing paper and start again, but online, yeah, we just do everything on the same sheet of paper or [inaudible 00:33:10] box if you don’t have paper laying around. And then, I guess, what is interesting in this exercise is that at the end once we have created our maps and we have plotted our data stories, we swap them around and from being the creators we become the readers. And there is plenty of learning that can actually be drawn by just doing some really rapid user testing and see how people actually enter these maps, what they find useful, how they travel perhaps back and forth between the key and the visualization, what type of titles are the most interesting, most successful, and ultimately, also like, it’s important to know the bias they will put as creators are also mirrored in the bias that we put as readers. So whenever we create our maps, obviously, we put ourselves in the middle of this creation, we see the country where we’re from and blah, blah. And when we’re reading this visualization, ultimately, what we do is overlaying our own map on top of this world maps, to find if the creator actually did include our country or did include the places that we know. And that’s actually how we read data visualization, like, there isn’t a universal way to depict a dataset, and there isn’t a universal way to interpret the chart because everybody has a unique experience, and there’s a unique understanding of the specific data. So yeah, in summary, that’s kind of like the icebreaker of the workshops. JS: Really interesting. And when you prime people to start adding data to their maps, do you show them examples or do you say, here are some data types that you could plot, because when I’ve done this and you had inspired this exercise for me when I teach kids – and when I teach kids I have them just draw the one floor in their house, and I feel like the times when I show them a drawing of my own house and then I draw circles in each of the rooms of how much time I spent in each room, I get a bunch of kids who start drawing circles on the map. And so it’s a double edged sword because on the one hand they may not know how to add the data to the map, but on the other hand I don’t want to prime them to just be using circles. VD: Yeah, it’s a fine balance. I found the same, and I run the same exercise with kids myself, and usually kids tend to just follow the instruction, which is fine. At the same time, with [inaudible 00:35:55] audience as well, like, you might have the audience kind of like stuck and needs a bit of prompt and help, and that’s totally fine too. But, I guess, like, I always try to suggest a few paths, like, it could be travels, it could be food, but what if it was your unique story, what could it be, and kind of like rewarding as well of like saying the most creative or the most unusual story would get [inaudible 00:36:25] in the clouds or something like that usually does prompt a bit more inspiration or the challenge at least. JS: You have a workshop coming up, right? VD: I do, on the 6th and the 7th of July. JS: And it must be virtual. VD: It’s virtual, yes. [inaudible 00:36:43] one of the many Zoom [inaudible 00:36:47] yeah. JS: Many zoom meetings, right. So do you want to just talk about it real quick, and I’ll put a link in the show notes in case people want to [inaudible 00:36:54] VD: Sure. So it’s a full-on deep dive into the process of infographic storytelling and there’s visualization, the activity that I just explained right now would be probably included, as many others. We’re going to be looking at conventional charts as well as the use of visual metaphors, storytelling devices, interactivity versus linear storytelling, and just a lot of visual communication and visual perception and hopefully a lot of fun. So yeah, if you want to join, for anybody listening, it would be amazing. JS: Yeah, I’ll put the link on the notes page, and people could check it out. And I also will put links to the various projects that you talked about and, of course, your whole site and the book which is great, I have it here somewhere [inaudible 00:37:49] my bookshelf. Great. Well, it sounds great, sounds like you’re doing great. Thanks so much for chatting with me. And yeah, it’s been great chatting. Great to hear from you. VD: Thank you so much for having me Jon. Thanks. I hope you enjoyed that interview with Valentina and I hope you learned something and maybe can incorporate it into your own work. Take a look at Valentina’s website, her portfolio, and her classes with the Guardian, all linked on the show notes. I hope you’ll consider leaving a review of the show on iTunes or Stitcher or Spotify or wherever you listen to podcasts. I hope you’ll share it with your networks. And if you’d be so kind to support the show financially, head over to my Patreon page for just for a couple bucks a month, you can help me pay for things like transcription, sound editing, and more. I hope you will have a lovely restful healthy summer, and I look forward to connecting with you all again in the fall. So until next time, this has been the PolicyViz podcast. Thanks so much for listening. The post Episode #178: Valentina D’Efilippo appeared first on Policy Viz.
47 minutes | 5 months ago
Episode #177: Christine Zhang
Christine Zhang just joined the Financial Times as a data journalist on the US elections team for 2020. Previously, she was a data journalist at The Baltimore Sun, where she used numbers, statistics and graphics to tell local news stories on a variety of topics, including police overtime, homicide patterns, population demographics, local and statewide politics — and even made a series of plots visualizing the impressive performance of Ravens quarterback Lamar Jackson. Prior to joining The Sun in 2018, she worked at Two Sigma in New York City, the Los Angeles Times in Los Angeles and the Brookings Institution in Washington, D.C. She has a B.A. from Smith College and an M.A. from Columbia University. Here’s a link to the FT-Peterson poll: https://www.ft.com/us-economic-sentiment-poll Episode Notes Christine on Twitter Baltimore Sun | Christine’s profile at the Sun Baltimore Sun Lamar Jackson story FT-Peterson Survey Financial Times: Trump vs Biden: who is leading the 2020 US election polls? Support the Show This show is completely listener-supported. There are no ads on the show notes page or in the audio. If you would like to financially support the show, please check out my Patreon page, where just for a few bucks a month, you can get a sneak peek at guests, grab stickers, or even a podcast mug. Your support helps me cover audio editing services, transcription services, and more. You can also support the show by sharing it with others and reviewing it on iTunes or your favorite podcast provider. Transcript Welcome back to the PolicyViz podcast. I’m your host Jon Schwabish. On this week’s episode, I’m very excited to chat with Christine Zhang. Christine was a reporter at the Baltimore Sun until very recently when she started a new exciting endeavor which we talk about in this week’s conversation. I was excited to talk to Christine because I’ve talked to a lot of people in the media sector who work at national or international news organizations, and so I was excited to talk to Christine and get that local perspective of what it means to work in a local newsroom. So I hope you’ll enjoy this week’s episode. Before we get to that, very quickly, if you’d like to support the show, please share it with your friends, your family, your neighbors. Please consider writing a review of the show on any of the major podcast providers that you might listen to, Spotify, Stitcher, iTunes, Google Play, and so on and so forth. And if you’d like to support the show finally, I’d also really, really appreciate it, just a couple of bucks per month helps me transcribe the show, helps me pay for sound editing, helps me pay for web services all the things that I need to bring this show to you every other week. So let’s get on to the show. This week’s conversation with Christine Zhang. I hope you’ll enjoy it and I hope you’ll learn something. Jon Schwabish: Hi Christine, how are you? Christine Zhang: Hi, I am doing well. It’s a rainy day here in Baltimore, and I am on my way to move into New Jersey, so it’s definitely an interesting time of transition. Moving is stressful enough but… JS: Enough, right, you are moving at the most inopportune time. CZ: Yeah. I don’t recommend it to anyone who’s thinking about it. JS: Right. So we’re going to talk a little bit about why you’re moving in a little bit. So we’ll let that hang in the air for people, so they can… CZ: Yeah, some suspense. JS: Some suspense, yeah, for the show. So I’m excited to chat with you about your past work and your current work and your future work which is really exciting. So maybe we’ll just start with – you can just introduce yourself for folks and a little bit about your background, and then we can just chat. CZ: Yeah, sure. So I am a data journalist, that’s my official job title, and currently I’m working at the Financial Times where I just recently started this spring, mainly to cover the US election this year. And before that I was in similar roles at the Baltimore Sun, hence why I’m in Baltimore currently as well as at the LA Times. But I actually have a pretty varied background, my interest in data journalism had only started just a few years ago before I became a full time data journalist. I was a research analyst at a think tank, the Brookings Institution in Washington DC. And so I’m actually doubly excited to be on this episode, because I feel like, I mean, I don’t know if this, but a lot of the data stuff that I was exposed to in DC including the PolicyViz podcast and blog were what really inspired me to go into data journalism in the first place and explore different ways to communicate and visualize data in the news. I’m not just saying that like – if you want to check my Twitter timeline, it’s true. So it’s really cool to be here. JS: That’s great. I’m glad we’re able to talk. It’s interesting because it’s actually been a few folks from Brookings who have gone on to data journalism like Chris Ingraham at the Post. CZ: Yeah… JS: [inaudible 00:03:40] for a long time. CZ: I think he and I didn’t overlap; I think when I started there, he had just left for the Post; but he’s been one of my data heroes/inspirations as well, and I’ve met him a couple of times and he’s just really, really cool. JS: Oh he’s great. And here’s a little background treat on how I know Chris. So Chris and I knew each other, I was at CDO at the time, he was at Brookings. And somehow we met up and started talking about our love of data and DataViz, and we decided to try to get together and learn D3 together. It was Chris, me, and one other person at Brookings, and after a few weeks, I was just like, okay, this is just – I’m not going to be a D3 programmer, I can see it, it’s just not going to happen. CZ: But that’s really cool though. JS: Yeah, that’s my link to Chris. From this background, you don’t necessarily have a perspective so much in looking at a local newspaper like the Baltimore Sun to a national newspaper like the Post or the Times, but I’m wondering if you can give us maybe just a bit of your experience of working as a data journalist at a local newspaper like the Baltimore Sun, and I’m sure there are listeners out there who will be able to relate or have some perspective on that more than I will, of course, but just what does the day to day look like at a local newspaper when you’re doing data journalism, and maybe not, as I think a lot of people at the big newspapers or the national newspapers are working on, what might be like bigger projects and national projects? CZ: Yeah, for sure. Well, I mean, first of all, I think, right now, is a really interesting time to think about local versus national/international news. So much of the news coverage has been dominated by COVID-19, and rightly so, since it’s such a big issue of our time, but I think for me it really highlights the comparative advantages of the different types of news organizations. Right now, at the Financial Times, there’s an amazing data team that I’m a part of and many of my colleagues in London have been tracking COVID-19 from an international perspective, looking at different countries, and how their trajectories have evolved. And to get that perspective, I think places like the FT are really great, and even to get like a national perspective as well for the US. But I think as a person living in Baltimore, for instance, like I am now, I obviously still follow the Sun to get updates on my own community and what’s happening at a neighborhood level here, and that’s not something necessarily that the Financial Times would cover because it’s geared towards a more global audience. So I think it’s really interesting because I think it really, for people in general, highlights the different ways that both can be very useful to people. And for me, working at the Sun was really an incredible experience I have a lot of personal attachment to the Baltimore area, it was actually the first place that I lived when I moved to the United States from China. So it’s been a lot of years and I’ve lived in many different places since then, but I took the job at the Baltimore Sun in 2018 because I wanted to follow my dream of becoming a data journalist ever since, as I mentioned, I lived was in DC, but also because I kind of wanted to rediscover this place from my childhood and really understand it and how it’s evolved in the intervening years. And my first story for the Sun actually represents both of these things, so my first story for the Sun was called the gender gap is real – for crabs. It’s kind of a strange title, but basically, I was updating a project that my predecessors on the data team at the Sun had published that tracked the price of steamed crabs in more than 30 local crab houses in the area. So for those of you who don’t know, Maryland is famous for its blue crab and eating it and how to eat it is like a whole thing. I’m not going to get into that now, but the point is this is like actually a pretty useful “public service” dataset. It’s living in the Baltimore area and you want to know where you can find the cheapest steamed crab or even the most expensive one or the closest one to you, you could go to this website and find out. And we had used a dozen male crabs as the comparison point, so we would call all these places and ask them how much they charged for a dozen males. But when I was calling these places, I remembered that while I was growing up, my parents and I, and a lot of their Chinese-American friends, had actually preferred eating female crabs to male crabs. So I started asking all of these places that I was calling anyway for this project how much crab houses were charging for female crabs. And it turns out that the charge for female crabs was much less than the price for males. So yeah, the article is like a first person essay that talks about the reasons for this price disparity or this “gender gap” which, I think if you – I did calculate it, on average it kind of matches the average pay gap between the males and females in the US. So it’s like a bit tongue-in-cheek. I’m not like – it’s not that profound, I’m not going to win a Pulitzer for it. I didn’t, spoiler alert. But I do think it’s interesting, because it’s not just a matter of size or biology as you might think, it’s actually a question of culture. So I mean, that was just really great to work on. It includes a fun lollipop data visualization that visualizes the gender gap at each house, so that was really great. And so one of the really great parts about working in a local newsroom is that there are so many opportunities to work on lots of different kinds of stories like the crab prices story, which was just kind of, I don’t want to say random, but definitely an atypical sort of story, and a very personal one to me but it also had data. So it was like a really quirky thing. And I think for better or for worse, the data team at the Sun was pretty small. When I started there, there were three people including me. By the time I left, it was just me on a full time basis, and the entire newsroom is only about 80 or 90 reporters, editors, and photographers. So you can imagine what the challenges are, and I think anyone who’s worked in a local newsroom can imagine those challenges. But on the other hand there wasn’t a whole lot of room for territoriality among beats or anything. It wasn’t like, I don’t know, there was like a crab correspondent who was like, no [inaudible 00:10:53] JS: It won’t totally surprise me if there was a crab correspondent. CZ: Maybe someday, yeah. So I think that was nice. I got to work with lots of different people, collaboration was really key, there were – for me anyway, I didn’t feel like there were too many silos. I think a good example of that is one of the projects that I did which involved some collaboration with the sports department. So Lamar Jackson, the quarterback of the Baltimore Ravens, was going to be named the MVP. And basically, everyone knew that that was going to happen or at least we definitely thought there was a high probability, and so the Sun decided to create a special section dedicated to him; and they asked me to work with Tracy Gossen of Baltimore Sun, designer, to create a two-page graphical spread visualizing some of Lamar Jackson’s main achievements; and I’m not the biggest sportsperson, and I only recently learned – this is embarrassing – I probably, I could tell you the basic rules of football, but that’s basically it, that’s it. So I was like, oh my god, I don’t even know where to start, like, I don’t know anything. But I actually think it’s a great example of why you need to have somebody with subject matter expertise in addition to somebody with data expertise or visualization expertise, because if you don’t, then you could end up creating some random chart that makes no sense whatsoever or that doesn’t actually take into account the different nuances of football and what data points really matter. So I’m really indebted to a lot of reporters who are very patient with me in describing the rules of the game. JS: So when you created that piece, because I think when I first saw that piece, I think someone had taken a picture of the paper version, and that’s where I saw it first, and then I saw it online – so with those types of pieces, are you designing print first, online first, or just both simultaneously? CZ: Yeah, that’s a really good question – this is interesting because I think as much as a lot of people like to say things like, oh print is dead, or, it’s all digital first all the time, I think in certain instances, like special sections of newspapers or magazines, the print version can be as compelling or even more compelling than the digital version. And in this case, because it was designed to be almost like a collectible section, not just my part in it, obviously, it was an entire special [inaudible 00:14:05] section, it was kind of designed to be a standalone collectible portion of that newspaper. We tried to design it so that it would fit for the confines of print before translating it to the website. JS: So you spent some time in Baltimore working on local news, although I think Lamar Jackson was bigger than just local. Now you’re moving to New Jersey and you’re working at a different kind of place. Do you want to talk about your move and the work you’ll be doing or already are doing, I guess, over there? CZ: Yeah, sure. So I’ve been working remotely for the past couple of months. Well, I guess, not just me, everybody is now. Sometimes I forget, because it was only supposed to be me, but now everyone is. I got a new job at the Financial Times, and this job is to be the US election’s data journalist, basically focusing on the 2020 Presidential Election in the US which is huge, I mean, it’s definitely a very big election year. And I think it’s almost coming full circle because 2016 was my first “real job” in journalism, and I was at the LA Times on their data team, and that was also, as you know, a very different and unprecedented election year. So yeah, it’s been pretty awesome so far. JS: Yeah. And so, it’s weird right now, because the election seems to – well, not seems to, has taken a backseat in a lot of ways to where we all are. So do you want to talk a little bit about the other work that you’ve been doing, and maybe how you’re laying the groundwork for the political coverage you’ll do over the next however many months, six-nine months, wherever we are? CZ: Yeah, sure. It is weird. First of all, I want to take a step back and just think about, after 2016, when I would give talks about data journalism and what it means and what do data journalists do, my most prominent example was like polls and election forecasting. Since post 2016, that really started to become, for better or for worse, what people associated with data journalism the most. So all of my talks were all about, well, data journalism encompasses political polls and, in some cases, election forecasting, but there’s a wide range of topics that data journalism could possibly cover, including football or crabs or crime rates. All of these things could have data points, maybe you don’t notice it as much but it definitely does. Now though, I really think that in some ways, COVID-19 has been what people think of when they think of data journalism. I don’t know. I’m positing this as a theory and not as a definitive thing. I mean, I don’t know. But I kind of feel like, whereas like polls were what people associated with data journalism the most in the past, now it’s like coronavirus trajectory charts. And I think there’s actually a lot of parallels in that, like, with election data, there are so many nuances to the ways that things are presented, like, uncertainty and margin of error and what a forecast actually is and means, that can cause a lot of confusion. In the same way that data on coronavirus can also cause some confusion. There’s been, I think you did a podcast episode about DataViz in the time of COVID, and certainly, not all the data are necessarily reliable or maybe there are some delays that may make cases appear to be decreasing, when they’re actually not. So it’s a really nuanced thing as well. But yeah, so obviously, as I just laid out, the coronavirus pandemic is really taking over a lot of the traditional election coverage. And I think for me, I’ve kind of been moonlighting, I should say, a little bit as the Baltimore correspondent with regard to certain aspects of coronavirus. One of my colleagues in London, Federica Cocco, she’s a statistics journalist who does a lot of videos that explain different things in series of charts, and she had an idea of looking at crime in the time of COVID-19, and I also had independently thought of that in my mind – for Baltimore, for instance, it has the highest murder rate among large US cities. I was wondering if the fact that people might be staying in more would have an effect on homicides in the city. And Federica had thought of a similar thing with regard to violent crimes in London. So she asked me to pair up with her to maybe talk about the US perspective, starting with Baltimore and expanding to different cities on how crime has evolved post lockdown, not post pandemic unfortunately, because that won’t come for some time. But it was an interesting exercise, because I think, for me, it’s like, I know Baltimore crime statistics, I know all the nuances of those statistics and what to look out for in terms of interpreting them. But I had to really think about ways to expand beyond just talking about Baltimore and figure out a theme to apply, not necessarily to the entire US, but to many other US cities. So I came across a news story from the Trace which is a nonprofit newsroom that covers gun crimes in the US, and it mentioned how shootings may be an exception to the decrease in violent crimes. So this is where I plug the programming language R, which I use for a lot of my data analyses, and where I give a special shout out to Daniel Nass who was the author of that Trace article, because I asked him to send me some of his R code that outlined how he was calculating crime statistics in different cities, and I modified it and applied it to a selection of cities for this crime video. And basically, the upshot is violent crimes have, in general, decreased post lockdown. I think that it might be pretty intuitive if there are fewer people outside, there might be fewer opportunities for robberies and things like that. But gun violence, shootings, assaults, homicides have in most cities either increased or not gone down at the same level as other violent crimes. JS: For you personally, what is that shift like going from, you know, if you were doing the same story at the Sun, it would have been presumably just focused on Baltimore, maybe neighboring cities like Philly and DC, but now with the FT, you’re looking nationally and internationally, so for you, what is that like having to do that shift? And also, what does that mean for your ability to tell the stories with data, it seems like it’s a little bit – I don’t know, is it easier – I guess, I’ll just ask: is it easier or harder to tell those stories when you’re dealing with hundreds or dozens of cities as opposed to just one city in the neighborhoods in that city? CZ: I would say that it is, in those cases, for me, slightly harder. I think one of the maybe advantages of working in local news is that you could, if you have a national dataset, you focus on the part of that national dataset that applies to the city that you live in or the coverage area of the news organization that you’re working for. But when the audience of your news organization is so much broader, then you have to broaden the perspective accordingly. So I think Baltimore is interesting in some cases on its own, like, from a national or even international perspective, but I think that particular video is strengthened by the overarching theme of violent crimes are down except for certain crimes in these places. So in the UK, that would be drug offenses. In many US cities, that would be gun crimes. And in Mexico, which was the other place that another data journalist, Jane Pong, looked at, it would be homicides. So I think it’s really interesting in the sense that it requires more creativity I think sometimes in terms of thinking of common threads or things in terms of weaving stuff together, but there’s also a lot more opportunity for collaboration across continents. And it’s something that, like, it’s not like I would necessarily think to look at what homicide trends in Mexico were like or crimes in London, the trends in crimes in London. So I think that’s interesting. JS: Yeah, definitely. Go ahead. CZ: I was also going to say, another aspect of working on the US election for the FT, even though, as you mentioned, a lot of things have slowed down in recent weeks, is that there is an aspect of writing for the US audience but also an aspect of explaining or making things interesting to an international audience. So things like Super Tuesday, which was my first article, I believe, for the FT was a Super Tuesday explainer. Now, I think like, certainly, from a US perspective, a lot of people could imagine why that matters. But I don’t think necessarily people living in other countries totally understand all of the intricacies of the US primary system. And honestly, a lot of people, I suppose, in the US, myself included, don’t understand all of the intricacies, I spent maybe, I don’t even know, hours, trying to find the answer to the question what happens to a candidate’s delegates if they drop out of the race which you think is like a simple question and actually at that point mattered because there were multiple contenders, but it’s actually a really complicated answer that gets into a lot of weird conventions rules that nobody has looked up, except for maybe a couple of scholars. JS: And you. CZ: And me. So I mean, I was really proud that we were able to answer that question. And I’m hoping to do a lot more of that as well, like, really try to ask and answer questions that may seem “simple” but actually are more nuanced. Another story that I worked on with my colleague Brooke Fox who’s in New York focused on swing states and what the polls in swing states are showing along with the demographic data. And one key question is what is a swing state, and what is a battleground state in the first place. And people might say, oh okay, like that, it just makes sense, because swing states are obviously states that, like, in 2016, Michigan and Wisconsin were states that really helped to decide that election. But the interesting thing is it’s not like back in 2016, people would have predicted that Wisconsin was going to be the pivotal state at this ceiling point before November. So I think it’s kind of like we’re just using the past to inform them what we’re talking about now, but that might not be indicative of what’s the most important thing to focus on in the future. But it’s also like, this is kind of the best that we have. So certainly, I think election reporting definitely it gets into a lot of deep philosophical questions. JS: You mentioned earlier about polling data, and I know the FT has this new monthly poll that they’re running, and I wanted to ask you to maybe talk about that a little bit, and maybe also along with that, since you’re going to be neck deep in polling data over the next few months, how you think about communicating either visually or – well, primarily visually, I guess – communicating the uncertainty and the margin of error in those polls as they come up. So that’s kind of a two-part question, so really on the new FT poll, and then on how, I guess, you, as a data journalist, think about communicating the uncertainty around those sorts of estimates. CZ: Yeah, that is definitely getting into the philosophical questions of data visualization and journalism. So the FT, this was before I arrived there actually, so the FT had partnered with a foundation called the Peterson Foundation, a nonprofit organization to conduct a monthly poll about US voters’ sentiment about the economy ahead of the election. And they’ve actually conducted it ever since October of 2019, so including May whose data I think we should be getting within the next week or so, we’ll have eight months of data from that poll. And it’s interesting because I feel like a lot of news organizations do polls and a lot of news organizations report on polls, especially during election time; and a lot of those polls with, like, I could say, with exception to questions about coronavirus, a lot of those polls boil down to, who are you going to vote for, Trump or Biden, and here’s the answer, and then we can discuss who has the lead. But this poll actually doesn’t ask those questions. It focuses on economic sentiment. So the main question that the poll focuses on is since Donald Trump has become President, would you say that you are financially – and there’s a set of answers. So it’s like somewhat better off, much better off, no change, somewhat worse off or much worse off. So basically, it asks whether voters think that they are financially better or worse off since Trump has become President. That’s not the only question, but it is the headline question of the poll. And it takes its cue from a question that Ronald Reagan had asked Jimmy Carter back in 1980, and basically, I think it was the week before election day. During a debate, he asked Americans to ask themselves, are you better off than you were four years ago. Jimmy Carter was the incumbent president. The economy was in a recession. Rhetorically, I would say, most voters answered no on election day because Reagan won in a landslide, of course. But the answer to that question is not so simple right now. I think that when the FT and the Peterson Foundation started this poll last year, the economy was in good shape and, I think, was for the incumbent President Donald Trump a way of signaling some positive sentiment for his reelection agenda. So at the time it was an interesting question because if most voters said that they were not financially better off, even whilst some of the economy was doing much better, that might signal something for the election. But now, it’s obviously the complete opposite. A lot of economic indicators including unemployment are at record numbers, there’s record high unemployment. So the question now is kind of like are people going to change their answers to that question over the coming months and will that signify something for the way that they’ll vote in November – essentially, like, will the economy matter and to what extent it will? And I think it’s interesting because I have seen just by looking at the past months of data for this poll that the answers are highly partisan. So only 11% of Democrats say that they’re better off since Trump became President, 63 or so percent of Republicans do, and those numbers haven’t changed that much since October. It’s kind of like basically two flatlines one way above the other, and it might show that despite these historic record setting economic indicators in a bad way, historically, negative economic indicators, partisanship is still extremely important in terms of the way that people view their own individual situation. And it might be like almost a referendum as to whether partisan identity is more important or at least more on people’s minds than numbers on certain statistical indicators. JS: Yeah. Well, I think it’s also personally your individual experience. I think a lot of the commentary I’ve seen about COVID, for example, is a lot of people who are arguing that we don’t need masks and we don’t need socially distance and all those techniques and approaches that public health experts are talking about, a lot of people who are against those approaches, there hasn’t been a huge outbreak of COVID infections that we know of or deaths in their area. So as soon as you start to see your friends and your family and your colleagues and coworkers start to get sick, I think it says something different, same thing when you start to lose your job versus you sort of see 30 million people losing their jobs, maybe it’s just a different, you make a different connection to it. CZ: Yeah, I think that’s true, for sure. It is interesting how people view their own personal thing versus a more abstract question. I will say, there’s only one other poll that I could find that tracks a similar question, and it’s the Economist/YouGov Poll, and it asks the question: is the country better off now than it was four years ago? And so to your point, 50% of the respondents, as of the beginning of May, have said that the country was better off four years ago and 30% say that the country is better off now. But again that is like so divided by party ID, like, I think, again, a lot of answers to these questions boils down to partisan [inaudible 00:36:14] so I do think that that’s something to keep in mind. JS: Yeah. Before I let you go and get your truck packed, [inaudible 00:36:27] I wanted to come back to this concept of uncertainty. I mean, have you been working with the FT, the current FT poll? CZ: The FT-Peterson, yeah, I have. JS: So working with that and then looking ahead, like, how do you think about visually at least communicating the uncertainty and the margin of error, we’re all going to see that 3% polling margin of error number come up over and over and over again in a few months, so have you and maybe folks you’ve worked with talked about how the best way to communicate those errors and the uncertainty around them? CZ: Yeah, we’ve thought about it a lot, every month actually Lauren Federer who is a DC correspondent for the FT, and I worked together on a story about the latest findings in the poll, and I’m also in the process of designing a site to house the now eight months’ worth of data and to highlight the key findings. I think deciding whether or not to visualize things like the margin of error is kind of a tricky thing, for one, it’s not entirely clear to me, at least, how you would do this. So say for the month to month answers to the better off question, right now, we’ve decided to visualize this on a line chart with two lines. So one line month to month shows the percentage of those who say that they are better off since Trump became President and another line shows the percentage of those who say that they’re worse off, and each of those lines will have a ribbon around it that represents the confidence interval. It’s supposed to be a visual display of the zone of uncertainty which for any given month is about plus or minus three percentage points. So if it’s like 30%, then we do like 33% would be the max and 27% would be the min – and then that would be the zone of uncertainty. And I think intuitively this makes a lot of sense, but it’s actually kind of misleading from a stats math perspective and going back to my college stats classes, because what we’re doing is drawing a confidence interval for the better off answer, and a confidence interval for the worst off answer separately, 95% confidence interval, with like, again, why is it 95%, like, why don’t we do 90%, like, I’m not even going to get into that, like, we’re just [inaudible 00:39:06] just to make things simple. But really the correct thing to do is actually to draw a confidence interval for the difference between the two and visualize what the difference is. So I think maybe it’s easier to think about in terms of candidates – like, if Biden is leading Trump by five percentage points, is that five statistically different from zero? That’s like the “correct” way to think about it, not like the individual percentage point that Trump has and the individual percentage support that Trump has versus the individual percentage support that Biden has. It’s the margin of error of the difference between the two is actually the correct way to think about these things. I think that there’s a Professor Charles Franklin that I found a paper by, he wrote something called the margin of error for differences in polls. That has been instrumental to my explaining this and thinking about this, but it’s all just to say those lines that you see with the ribbons around them are only visualizing the individual confidence intervals of each person or each answer choice. But what you want is if you truly want to find out if one is leading the other is to visualize the confidence intervals around the difference between the two. But that makes for a less compelling visual, like, I don’t know… JS: [inaudible 00:40:54] super complicated for people to understand too. CZ: Yeah. So it’s not like an easy thing. And also, if you have more than one answer choice, then you’re just like, it’s going to look just really, really confusing. So I think the best thing to do is just talk about the margin of error in the text or to indicate it in a footnote. In a lot of the stories that we have done over the past few months about the Peterson polls, we do try to indicate what the margin of error is not just for the poll itself which is plus or minus three percentage points, but also for the different subgroups in the poll. So most polls actually do have a margin of error of plus or minus three percentage points, and that’s not a coincidence. It’s because most nationwide polls sample about a 1000 people. The formula for margin of error is roughly equivalent to one divided by the square root of the sample size. So one divided by the square root of a 1000 is 3.167, no, 3.162, I just did this. So like basically plus or minus three. So you don’t even have to tell people how many people you’ve sampled, they say plus or minus three percentage points, you basically know that’s how much. So that’s fine if you’re looking at the overall top line questions, like, are you better off versus four years ago or whatever. But when you start looking at different subgroups like Democrats, do Democrats say that they’re better off than they were when Trump was elected? Now the poll, this particular poll only asked 300 or so Democrats, and so that sample size is now 300. So one divided by the square root of 300 just like back of the envelope calculation here is like 5.7 percentage points. So that’s different from three percentage points. JS: Yeah. CZ: And I think it’s hard because when people see this is a poll with a margin of error of plus or minus three percentage points, they automatically add and subtract three percentage points from every percentage they will see an article even though in some cases it is like almost double that. JS: Wow. Yeah. CZ: So like it’s hard. It’s not… I don’t think it’s easy, but I do think that it’s worth the explanation in many cases. JS: No, it’s a good important challenge, and one that there’s a lot of room to try to solve and to experiment with. You’re kind of in that unique position where you can try lots of different things and see what people respond to. CZ: Yeah, that’s a good point. I was going to say, I don’t know how data visualization can solve this because I feel like my answer was like, write it in the text or put it as a footnote which is not really DataViz. JS: Yeah, right. CZ: But then if you do something like the needle, I don’t know, like [inaudible 00:44:18] completely freak out. JS: Yeah. But to that point, when you try to do, I mean, the needle maybe kind of the extreme example – and for those who don’t know, we’re talking about, I’ll put a link on the show notes, it’s this needle gauge chart that the New York Times did in the 2016 election about probability of winning, but I think trying some of these other graph types out, you run into this problem that people don’t necessarily know how to read these other graph types. And so you end up in this bit of a Catch-22 of do I try a graph that maybe shows the distribution in a better way but fewer people really understand how to read it or do I try a bar chart, but then how do I show the uncertainty in a better way than just an error bar. CZ: Yeah, it’s definitely a learning process. But I think that the learning is happening. I think that, for example, I think in 2016 when Clinton won the popular vote but Donald Trump won the electoral college, that was such an anomaly. I mean, I think the last time it happened was 2000, and that was so exceptional for people. I think that now there are a lot more election reporters, myself included, that take that as a possibility or take a look not just at the national polling average but also at the state level polling averages. So I think it is an incremental thing, but it’s happening. JS: That’s great. Well, that’s great. Well, thanks for chatting with me. Thanks for coming on the show and good luck with the move. I look forward to seeing all the stuff you’re going to be working on and coming out with over the next few months. CZ: Thank you so much for having me. Thanks everyone for listening to this week’s episode of the show. I hope you enjoyed it, I hope you learned something, and I hope you’ll be able to take some of the lessons from this week’s episode and apply it to your own work. So again, I hope you’ll consider supporting the show either by sharing it, by writing reviews or even financially by going over to Patreon. So until next time, this has been the PolicyViz podcast, thanks so much for listening. The post Episode #177: Christine Zhang appeared first on Policy Viz.
58 minutes | 6 months ago
Episode #176: Manuel Lima
Manuel Lima is the author of three books and a leading voice on information visualization. He has worked with an array of organizations designing digital experiences and leading product teams. On this week’s episode of the podcast, I’m reposting a video conversation I had with Manuel as part of the Data@Urban Digital Discussion series. We talk about his love of circles and trees, and more. Episode Notes Manuel’s website The Book of Circles The Book of Trees Visual Complexity Support the Show This show is completely listener-supported. There are no ads on the show notes page or in the audio. If you would like to financially support the show, please check out my Patreon page, where just for a few bucks a month, you can get a sneak peek at guests, grab stickers, or even a podcast mug. Your support helps me cover audio editing services, transcription services, and more. You can also support the show by sharing it with others and reviewing it on iTunes or your favorite podcast provider. Transcript Jon Schwabish: Welcome back to the PolicyViz podcast. I am your host, Jon Schwabish. I hope you, and your family, and your friends are all healthy, and safe, and well in these strange days. On this week’s episode of the podcast, I’m going to once again repost one of the recent data at Urban Digital discussions. This time, the one that I had with Manuel Lima, Manuel, as you may know, is the author of Book of Circles, the Book of Trees, the book and website VisualComplexity. He is a creator; he is a speaker; he’s an author. And we had a really good time chatting with one another and taking questions from people who attended that live video chat. So I’m not going to talk about anything else, and I’m just going to get right to the episode. So I hope you’ll enjoy this episode of the PolicyViz podcast with Manuel Lima. Hi, everybody. I’m Jon Schwabish from Urban Institute. Thanks for coming in to this digital discussion, chat. Good to see everybody. And I see a bunch of people who’ve been here for the last few days, which is great to see people coming back. So yeah, love it. So I’ll just set up the parameters of the chat. So as you can see on your screen, Manuel Lima is here to talk to us about all of his awesome work. So this is going to be great. If you have questions, just put them in the chat window, and I’ll try to make a bit of a queue and then, you know, you can just unmute yourself when it’s your turn, and you can ask Manuel your questions. There’s no need for me to have to read them unless you don’t want to unmute yourself. That’s totally fine too. And that’s it. It’s pretty low key. There’s not a lot of polish to this. So just a chance for all of us to connect and have some conversation maybe with some adults for an hour and let the kids go have some screen time or whatever. So, yeah. All right. Manuel, how’re you doing, buddy? Manuel Lima: I’m doing great. Thanks. Thanks everyone for, thanks everyone for joining. And thanks, John for hosting me. This is, this is a great initiative for sure. JS: Well, I’m glad you could come on. So I’m going to, I’m going to show you have other books. So this is the newest one, right? Is that right? ML: No, it’s the second. The newest is Circles, actually. JS: The newest is Circles. Okay. So here’s Trees. So here’s the Book of Trees. And here’s the Book of Circles. So we can talk about circles and maybe pie charts. And you can, we can argue about that. These are both beautiful books. So I thought maybe we would start, you could just start by telling people about yourself. Again, it’s whatever. But I mean, what I as we were talking about before we kind of started, what I love about both of these books is they feel that the physical version, it’s super tactile; it has a great feel to it, the print, the color. So maybe I don’t know if you want to talk about the process of creating these books. And in your, in your process because there’s, it’s all historical. So what is your process like to go through and, you know, research all this material? And then maybe we can just after that we can, we can segue into what you’re doing now and then people can sort of post their questions. We just take it like that. ML: That sounds, that sounds great. JS: Great. Yeah. So go ahead. Why don’t you go ahead? ML: Yeah. So you mentioned the quality of the books, right. That’s something that I also feel very proud of, of achieving in many ways with these books. And I think really, the credit goes to Princeton Architectural Press, the publisher. They really invest a lot of time in making the great books, high quality paper, you know, high quality printing colors. They really strive to do high quality work. And I think it’s really at a time where we have a lot of really cheap, cheap books that are just really low quality and I hate to actually consume those books. I would rather have a PDF. In those cases I think it hurts everyone, right? It’s not a good experience. It kills more, more trees along the way. And it’s just a really like, yeah, suboptimal experience for sure. So, huge proponent of like high quality books, I think it’s actually one of the quality, one of the types of books actually going up in terms of numbers. Because people that care about books care about high quality, right. JS: Right. ML: So, so yeah, people that are new to my work, I would say, maybe read my books in reverse order, which is starting with the latest, and then navigating all the way to my first which is Visual Complexity. And I say this because like, when I started Visual Complexity, and this Visual Complexity for those who haven’t seen the book, it’s really all about network visualization, right? And it tries to understand this new sort of phenomenon of obsession for networks and visualizing really complex intricate structures. So as I was doing the book, right, and even the first chapter of that book is called the Tree of Life. And that was me trying to go back in time to understand the origin, right, the genesis of interest from humans to visualize these intricate structures, which take, took me back to the tree diagram, right? And then I knew when I was actually doing visual complexity that the tree diagram as I was uncovering all these illustrations, all these medieval work and so on, I realized that this was too good to be just a single chapter, right? So at some points, making a whole book dedicated to the tree diagram had to happen. I knew it in my, in my mind, it had to happen. So that was my second book, the Book of Trees, which really covers almost 800 years of human exploration of mapping hierarchies in the shape of a tree. And there’s multiple cases there, multiple types and topologies for tree diagrams. And then I think following the same sort of mental exercise, I’m a little bit of obsessed about the origin of things. I wanted to go even further back, right? Yeah. I wanted to go even further back. That’s a great, it’s like almost slides, right? I wanted to go even further back, right, like, would actually the first time that humans were thinking about visualizing information, right. And it took me to circles, like some of the most primitive. It took me actually back, roughly 40,000 years back in time to the first category of, you know, rock carvings that people were making really around the world, and many of them were circular in nature. Some of the most ancient archetypes of data visualization is the spiral, the concentric rings, and the section circle, which is the reason for why pie charts could possibly be so popular still today. So that was fascinating to me. And as I was discovering a lot of this old material, I became more interested in the old material than the new, I have to say. And I think part of that is, is well, twofold. One is that we are very present oriented, you know, we might think that we are in the pinnacle of civilization and everything we’re doing data visualization related or else or in a different subject is very new. And it has never been done before, right. So I think I was trying to dismiss to find that, that sort of take that everything we are doing is right, which is it was, and I discovered so many cases where what we’re doing today is still just variants of what has been done in the past. So that was one thing I was just really becoming in, you know, falling in love with a lot of this old material. And then the other, the other reason for me to do some of these books was also to preserve some of the stuff that we were doing even still today. And one of the concepts that was frightening to me, and I discovered this 10 years ago when I was working on the visual complexity book is that notion of the digital Dark Ages, which is this idea that, you know, many, you know, maybe a generation or two from now we’re going to be able to look back at the current time and not being able to see, read or decode a lot of the artifacts, the cultural artifacts we’re producing. And that for me is a super scary outcome. Right? I mean, it’s almost like, again, like this is a very, and it’s not even hard to imagine, it’s already happening, like already there’s this article on, I think, BBC the other day on researchers trying to understand the work of a physicist, UK physicist, right, and having to go back into old drives that he had, and they couldn’t, they don’t have the technology to read that stuff done in the 80s, right. And this is just like 30 years ago or something. So it’s really frightening. So imagine, like the same process of all the amazing stuff we’re creating today not being able to be read or just consumed by future generations. That’s a really scary thought for me. So if anything, some of these books, especially one of the modern examples are also going to preserve them for posterity right for that, for those future generations in some ways? JS: Can I ask for people who are, so I would guess there, there’s a few different types of people who read your books. There’s people who are just fascinated with historical data visualization, and how it, how it has changed and evolved over time. There’s people who are interested in history in general, people who are interested in design and the origins of design, and there’s probably another camp of people who are data visualization practitioners working in the modern tools, and I wonder what you would say to someone like that, who, you know, they don’t really have a necessarily an interest in the origins of design or data viz. What would you say to someone who, who’s a practitioner says, how can I use this book in my current work? ML: Yeah, I mean, it’s, that’s a great question. And yes, to your point, I think that’s what appeals to me is that I, you know, since I can remember, I always hated to be part of one single label or being in one single box, right? I like multiculturality. I like, you know, just very sort of expressive ways of thinking and visual complexity from the beginning has been that. I’ve always been fascinated by the amount of people and different backgrounds of people reaching out to me from, you know, architects to biologists to, to artists to, I mean, the full gamut of almost roles. And I think the books touch that, you know, even some of the media covering the books. JS: Yeah. ML: It’s not just about data visualization. It’s tech media. It’s art. It’s design. It’s science, you know, I had like Nature and Science talking about the book. So it’s a book that really touches a good full gamut of professions which and I think that’s actually part of the goal, really, because I think that’s the true nature of some of the stuff that is portrayed in the book. It has that sort of broad appeal, and I really I like that aspect of it. Now to the data visualization community specifically, you can learn a few things, like one, of course, you can be inspired by both modern and ancient things, sometimes even more inspired by the stuff they did in the old days, because a lot of that stuff was done, you know, by hands, right. And it’s incredible. I always give this example of, of the [00:11:23 inaudible], this like medieval technique that they had, you know, that span about hundreds of years ago. And it was basically discs of paper that you could spin independently. And this was really similar to analog computers. They were actually able to create millions of combinations using a very, very simple process of just paper discs and annotations, you know, fantastic stuff, the kind of thing that really, it really portrays human ingenuity in a whole different way I feel, especially those, those old ones. So I think data visualization people I think, been inspired by, again, both modern and ancient examples, but then also understanding the logic behind some of these things, right. So one of the things I think I talk in Visual Complexity is the notion of [0:12:09 inaudible] minority, which happens in the medieval ages. And this was when people were being literally inundated by new information. There’s, there’s a great book called Too Much to Know. And it, basically, it’s an entire book telling portraits or stories of people in that moment in time. This is roughly 700 years ago, 600 years ago, being inundated by new data, like there was a huge growth of production. There was all this information coming from the ancient world, ancient Rome and ancient Greece, and people had to make sense of it all. This was like they were facing Big Data as we know today, right? Just in a different time. And graphic depiction, right, these are data visualization was a huge factor in it. And Origin of [0:12:53 inaudible] was an attempt to decode the main principles for portraying information in a graphical way that would allow the user to actually memorize at a later stage. So this is really the genesis of information design. So a lot of the principles we still use today were actually created in medieval times. So for any kind of school or person that’s interested in, in, in data visualization principles, and I’m definitely obsessed with principles. I think it’s a great way of looking at a practice. It’s through the main guidelines that it has. I think it’s great to look back and see what again, the Genesis what drove those principles in the first place? Are they have evolved over time? And now to a certain degree, many of them are still used today. JS: Right? Do you, I want to ask one more question about the books and then you can talk about what you’re doing now. Do you have a, you’ve mentioned already a few like these historical examples. Do you have a favorite from either the Trees or the Circles book? Do you have a, do you have a favorite, like medieval [0:13:49 crosstalk] example? ML: Yeah. I have it here. I think, well, it’s so hard to get one favorite, but one of my favorites, it’s super hard to have one. You know, it’s, I think, it’s like, when you do a book like this, you kind of, you know, you really fell in love with so many examples. And even yesterday, I was talking to my daughter, Chloe. And of course, one of the, one of the positive things about everything that’s going on in the world right now is that you get to spend, well, positive and negative at times. JS: Yeah. Yeah. Yeah. ML: Double edged sword. But definitely spending more time with the kids and then talking to them in a way that you, you didn’t had the time before. Right? So, I was, I was showing her a lot of like these old tea tree diagrams. And she’s very much into drawing and all that. So this is actually one of my favorite examples is that it’s actually a double page. It’s a [0:14:37 inaudible] the Book of Trees. It’s an example of an original tree. What I love about this is that if you notice, the, it’s, this is actually, this is a tree of morality, which is actually a very famous theme in medieval Europe. It basically portrays the tree of virtues and the tree of vices. And notice how the designer in this case creates a lot of sort of visual metaphors to indicate what’s good on this side, right? Notice how the tree is, is much more rich. There’s like color everywhere. The leaves are all fully green. There’s fruits coming out. And this is the tree of vices, what you should not do, right? And notice how some of the branches are actually kind of dying, losing color. And they play with all these visual metaphors to explain some really complex concepts back then. And again, these are things that were all done by hand. And again, just the amount of creativity that went through some of these things is remarkable. JS: Yeah, yeah, that’s great. ML: Yeah. You, you’re also mentioning the process. I think the process for this, I feel like I’ve completed my trilogy of books at this time. I think it’s kind of like the worst type of books you can do in a way, because not only do you have to write a substantial amount, and you have to do research on, on writing the right, the right things and telling it in the right way. So there’s a lot of writing and research there. But then the worst part, arguably, is actually getting the images themselves. That’s a really usually time consuming process of going after the authors. You know, sometimes the authors no longer exist. Even modern examples are equally if not even as hard to get. Because, again, the digital Dark Ages, a lot of these things done in the early 2000s are lost, literally lost, like they lost the code. They, the plugin doesn’t work anymore. They aren’t able to reproduce it. Sometimes it’s actually harder to get a modern example than a medieval like three to 500 years ago, right? JS: Yeah. ML: Then there’s that and then it’s just like the whole process of, of, of actually writing, putting the research, making the images, the layout for the book, getting, I remember getting this is 10 years ago, Visual Complexity, getting the first manuscript back from the publisher with rad annotations all over the manuscript. I think I almost cry that moment. I was like, I was naive to the point of thinking that, you know, when you hand off the manuscript to the publisher, it’s pretty much– JS: Done. ML: It’s like, hey, my job is done. I wash my hands. I can relax somewhere. And that was just the beginning of the process, right. And then all those phases, just, I mean, of course, now I know about the process, I don’t get as scared or depressed as I did 10 years ago. But it is, you know, quite a time consuming process for sure. JS: Yeah. Well, the other thing about, about these books, um, and again, I would maybe harder for the early ones is that the quality of the images are all very, you know, they’re all high resolution images. ML: Yeah. JS: So which, which is, I don’t know, is it harder to get those, the older, is it easier or harder to get the older ones in a high res image than the stuff today? ML: It’s actually easier. I mean, again, to be honest, like there’s a lot of material still today that’s probably hidden in dusty cabinets somewhere in New Zealand galleries, elsewhere. So we only know what we know. Right? And, and, and, but fortunately, things are changing. And a lot of these institutions are actually making a lot of their collections available online for people like myself. I can be in a cafe in Brooklyn, and consuming these old medieval manuscripts I don’t actually have. This is also something that I saved for, I think, you know, 20 years ago, it would take a lot longer to do any of the books that I’ve done, right, in terms of like, historical ones, because I would actually have to go physically to a lot of these museums in Europe and elsewhere and US and actually grasp it. I didn’t even know how long it would take to do the Book of Circles, for example, if I, if I wanted to do it like 30 years ago. So it’s making it a lot easier. And yes, a lot of these examples are in high res. Now, it’s surprising, like let’s say that I want to make an example, and this happened in Visual Complexity a lot. An early example of a network visualization done in 1996, the resolution of those images were so bad back then, right? ‘96, you were talking about like 400 pixels by 400 pixels or something like that. So you couldn’t do that in a book, you couldn’t reproduce it. And then, again, like a lot of the code was done, like the author was like, hey, I would love to help you. But I don’t think I can like, like there’s no way, right? Or it just would take so much time for them to do it that way. It’s just not worth it. So I ended up not including a lot of these examples, modern examples because of that, for that reason. The only impediment for all the ones sometimes is the money because a lot of these institutions ask for a considerable amount. And I don’t have that amount to pay for every single image. JS: I know. ML: I had to pick and choose, I had to pick and choose a few ones that I actually ended up paying a good amount. You have to pick and choose. I think, you know, the Library of France, for example, National Library of France has amazing, beautiful illustrations, but they tend to charge a considerable amount. So I picked a few, but not many. JS: Yeah. I’m finishing another book right now. And it’s like the Washington Post and the New York Times, like, they want, you know, a lot of money, so– ML: Oh, yeah. JS: And I won’t say how much, but it’s a lot of money. ML: It’s a lot of money. Yeah. JS: And so then, then you talk to, you know, more local newspapers, like I have a few from the Texas Tribune who they do great work and, and they’re more willing to negotiate, you know, to say, you know, they want this amount. It’s like, well, I have to pay for this out of pocket. Would you be willing to do this? And so, you know, it’s great when they negotiate, but when you get to the big organizations, they have a whole company that does it for them, so– ML: Totally right. And then, I think what people also don’t understand, like, and I get requests like this, like, hey, why don’t we make this illustration like larger and then the price is also an outcome of that, right? It matters, like the size matters, where it’s gonna go, it all has a price. So if you want like a full page, like, especially in the cover, you would have to pay a lot of money. JS: Yeah, yeah, it matters. Yeah. ML: It matters tremendously, for sure. JS: Um, you want to talk a little bit about what you’re working on now. And then I’ll just remind people, if they have questions, just put them in the chat box in the chat window. And then you can ask directly, but maybe talk about a little bit what you’re working on now and then we’ll see if– ML: Yeah, absolutely. For sure. So, all right. So I’ve been at Google now for, I think, four and a half years. And what’s been interesting for me at Google is that I think, finally, I was able to match or marry the two sort of separate lives that I had up to now. So all the research that you saw, the books that we just talked about, you know, talks and teaching on data visualization, this has already been done on the side, right? It hasn’t really paid the bills substantially, which is another sort of thing that people that want to venture into this world of publishing should know it doesn’t pay that well. JS: Yeah. ML: If you’re doing it for the money, you’re not going to go far. JS: Yeah. Yeah. You are fooling yourself. Yeah. ML: So but apart from that, so yeah, so I had to pay the bills, right, and especially now with two kids. So over the last 15 years I kind of I feel like I’ve lived like two parallel lives, you know, on ‘95 I was working for as a UX designer, UX lead manager in places like Microsoft and startups and at Kia back in the day and now Google. And of course, on the side, I was doing all of that, researching, teaching all that stuff on data visualization. But interestingly enough, I think now at Google, I was able to marry the two things. And now I’m leading a team of data visualization team building, a library, a component library of charts, all of Google really focused on cloud, but also we have internal clients across the company, which is really fun. And I think that’s also the kind of way that I like to approach data visualization is being as horizontal as silicon and pluralistic as possible. multidisciplinary, right? So, we are, so my team is really a lot of roles from designers to UX engineers to researchers. And we are also focusing on some more sophisticated types of visualization components, you know, things like network topology or flow diagrams or complexity, you know, timelines of that nature, not your typical bar chart or line chart. JS: Yeah. ML: And there’s a growing appetite for that, both internally right across different tools that we have at the company, but even externally as well, especially on clouds. And some of the cloud products are already importing and using and adopting our components. So that’s kind of like what I’ve been doing. And again, it feels good because I think for the first time I’m able to, to combine and unify these two passions that I had. And then on the side, I’m also thinking about a fourth book. It’s not going to be the Book of Triangles though. I got, I got that, I got that from a lot of friends and my wife like what was the next book? The Triangles book? Yeah. JS: The wise guy. Yeah. Real funny. ML: Yeah, exactly. The Book of Squares that would be quite the book, you know, I think I ended this trilogy of books of that nature. So I’m thinking about doing another one. And that’s going to be an, probably an announcement of that sort. It’s more geared towards the design community this time around. And it’s not going to be as visual. But I think it’s going to be exciting. I’m definitely excited about that book specifically. And then I’m also interested, especially at the time where we are now in kind of doing something like you’re doing, John. I think, people being kind of, you know, at home in these really tough times, I think, if anything else, it’s a great opportunity for us to invest in ourselves, rather than expand our mind a little bit, talk to other people and educate ourselves and expand our mind in other ways, right? So I just started a series of free webinars. I’m using Crowdcast, which is really a cool platform. I’ve been doing a lot of research on what’s the right webinar tools to use and whatnot. I give, I tried a couple of others, but I think Crowdcast is a really cool one because you can follow people there. It’s kind of like there’s this social dynamics, you know, into podcast. So I’m [0:25:00 inaudible] that. And I announced this last week, about the same time that you announced this talk, I announced the free webinars. And you know, these are three webinars that it’s going to be called the Evolution of Data Viz, the Language of Data Viz and the Principles of Data Viz. And all three webinars, 100 people each filled up in like two hours. JS: Wow. ML: Which is to say, I think, I was really kind of blown away by the demand and interests. And I think there’s really a lot of appetite both for the subject, and also, of course, as a result of a lot of people being at home right now. But I’m really excited about that. I think, you know, looking back, doing some introspection on the things that excite me and you kind of do that when you turn 40, I think there’s a little bit, a little bit of a middle, middle aged– JS: Middle age crisis. ML: Middle age crisis that’s going on. It’s like hey, why don’t we, what do I want to do with the rest of my life? JS: Right, right, yeah. ML: And for me, for me, it’s really about, you know, communicating my knowledge and, you know, and inspiring other people, you know, and the book is really, the books are reflection of that passion that I have for, for knowledge. So I think doing that through webinars is really fun, like, what you are doing right now. And I like webinars more than just being a static video that you do online because we have interaction, like, hopefully, we’re going to start that in two minutes or so, you can actually have people asking questions and interacting with others. And I think it’s, it’s still not quite the same as a physical type of, of experience or a seminar or talk. But it’s almost the same, right? At least there’s some interactivity between. And the fact that anyone can join across the world is to me super empowering as well. JS: Yeah. ML: You know, if you were to do this, in, let’s say, New York tomorrow, you would be really conditioning people by the money that they would need to spend, you know, to travel to New York, to stay in a hotel, to go to this conference, and not a lot of people can afford, but doing this now you have people all around the world. You know, the webinars I was looking at the data, I have people from all the way from, from India, China, different regions of Asia, Europe, US. It’s, it’s really like the pluralistic effort. And I love that. I really do love that. JS: Yeah. It also, and I mean not, not for these, but for other webinars where you do them for an organization, the advantages, you know, maybe only they can only have 20 or whatever number of people attend the webinar at that time. And if you say well, just record them, then they have their own library where they can, you know, other people in the organization can go back and, you know, hopefully learn from it. It’s, it’s different because you’re more like watching a video as opposed to having an interaction. But it’s, it is, I think, it’s, it’s certainly a challenge and we’ve been, we’ve been having conversations like these for some of these discussions with, with people, you know, last couple weeks. There’s obviously a different sort of, like, technologies that you need with microphones and headphones and all that, but it’s, and I think we all know like it’s really easy to put that video, the webinar window like minimize and check your email. So I think it’s, there’s a, there’s maybe a strategy, a different kind of strategy when you’re, when you’re teaching in a webinar than, than when you’re, you know, live in front of an audience. Well, we can, we can keep talking, but let me, let me just pause and see if anyone has any questions because there’s, you know, this is, this is intended to be a discussion. So there are no questions in the window now. But does anyone have any questions for, for Manuel and on the books, current work? You know, how he’s faring in Brooklyn with two kids? Either folks are sleeping or they’re, they’re too shy right now. So we can just, we can just keep talking. So I’m curious about the Google work. I don’t know how much you can talk about it. So you know, whatever you’re allowed to talk about, but so you’re building, is it building a library of graphic types for people at Google to use as a reference library? Is that, is that essentially what or not essentially, was that the goal? ML: Yes. It’s kind of twofold, right? I mean, one is that we, we have created this, what we call internally, the spec, which is basically a set of specifications or guidelines on how to use charts. And that has been widely sort of used internally as, as, again, as a list of guidelines or best practices on how to use charts to bring then many types of charts and applications. And then, of course, the other thing we are doing is we are creating a component library, like a UI component library that people can just plug and play our components into their own products. JS: I gotcha. ML: And, of course, the plugin plays doesn’t quite happen that in that way. There’s always a lot of handholding and customization that’s needed. And actually one of the toughest challenges for us to understand is where do we draw the line between doing a general low charting library for different teams and knowing where, you know, when we can actually spend a lot of time customizing them for, for the, for the specific needs of an internal client or partner team. Right? JS: Yeah. ML: But then a lot of these charts will be visible in internal products that never see the light of day by anyone externally. But also some of them will be in, you know, many cloud products, for example, that will eventually be seen by, by enterprises and companies and end users elsewhere outside of the company. JS: Yeah, really interesting. There was a quick question for you that the webinar, I put the Crowdcast webinar link in the chatbox, and it looks like it’s full. So Bridget wants to know what, you know, gonna be more in the future recording them, you know? ML: Yeah. There will be more in the future. Again, like I was, I was rigid. I was highly surprised by, by the interest. I think I put it on Twitter. And again, it took me four hours or two hours to just the whole thing to be filled. And these are, you know, 100 people each per webinar. So it was more than I was expecting. I have kind of like a 100, 120 people limit. And this is more like a technical limit. I kind of realized that after 100 something people that can actually doesn’t work so well and you start kind of having issues. And it also has, I have issues with the plan that I have, so I would have to pay a lot more for the stability that comes with having more than 100 people. So I’m definitely planning on adding on doing more of those. Again, I was not expecting this type of demand. And I think, people, which is great. It’s great to know that people are both interested in this topic and, and probably like, you know, as they are today, locked at home, they are even more eager to do something that takes them away from home. JS: Yeah. ML: So I’m definitely planning on doing another series. JS: So if you, uh, the one thing that I’ve been thinking about with these is picking a time. So like I’ve picked noon seems to work or right around noon seems to work because you hit the West Coast folks, you know, they’re three hour behind you and I. We are on East Coast time. And then, you know, I think the UK is now like four or five hours ahead and Germany is six hours ahead. So you kind of get that, but then you miss, you know, the rest of the world. So I haven’t really figured out like the best way to pick the time actually. Have you thought about, thought about that, like are you trying to pick a consistent time or just say we’ll just do kind of random time so that I try to get as many people as I can? ML: I’m actually doing, yeah, I thought about that. And I was kind of doing some research on what would be the right time. I think 11 AM Eastern Time as we are doing today. And I’m going to do that exact same time tomorrow. I think it’s going to be one of the best because like you’re saying we got still, it’s a bit early for, for the West Coast, but, you know, it’s manageable and it’s, it covers a lot of people in Europe. Some places in Asia, it’s already a bit late. But, you know, we didn’t go there. I think I was, I was pinging, someone was pinging me to make sure of the time. So it’s 11 AM Eastern, but then for them, I think there are somewhere in India, it’s going to be 11 PM. Exactly Two hours later. JS: Right. ML: Which is a little bit late, right, granted. JS: Yeah. ML: But it’s hard. Yeah, I don’t think there’s like a specific. I think 11 Eastern might be. JS: Yeah. That’s what I was thinking, 11, 12. I mean, also, for the West Coast folks, it’s 8 AM. But people don’t have to commute now. ML: Yeah. ML: So you can sort of show up without being ready for work, you know, because you don’t necessarily have to show your screen. So, Zainab, and excuse me if I pronounced your name wrong, but, but he’s in Pakistan says it’s nine o’clock there. So, you know, maybe that’s as far as we can kind of go at this 11 o’clock. ML: Yeah. JS: But there is a question about tools and resources that you would suggest for someone just getting started in data viz, I don’t know, if you have, you know, specific tools or things that you would, you would recommend, but– ML: Oh, I mean tools. What is the question? Oh, yeah, tools and, and? JS: And resources. ML: And resources. The field keeps changing. I mean, actually, I was putting together a few slides for this webinar tomorrow. And I keep mentioning the profusion of tools that exist now in data visualization is just incredible like when I joined the community, you know, it was still a very sort of academic practice. This was like 15, 16 years ago. You would have to actually know a lot of coding programming language instruction to make any substantial effort in this, in this, in this area. And you probably recall that, John, like there was just not as it, as it is today at all. So I think it’s for the best, but now it’s hard to keep track of how many tools exists is if, if whoever asked that question, I can actually send them a link of resources. I keep track of some of the resources. I think visualizing data actually has a good page. JS: Yeah, I was just going to put that in here. Yeah. ML: Yeah. That’s a really good page. I think it’s just, basically, for all types of data visualization related tools. It’s so hard to keep track. It’s like in hundreds, right. And, and I think, it’s hard to pick one because it really depends on what you need. Right? So in terms of like, what are you trying to achieve? What is your fluency with, with programming languages? It kind of there’s a full gamut. There’s a full range, you know, from, from being a completely nervous or design oriented person to being like really familiar with code. And that will change the number of suggested tools change depending on that. That’s a pretty good pitch to start off. And then books is the same thing, like so many books have come out. I think, maybe the angle of my. of some of my books are more historical sort of background visual culture. If you want to go more into like the deep, deep one, like the ultra books, there’s a bunch of others. I think even visualizing data also has a another link on books as well. But if you search, if that, if that person sends me a link, I will ping them to my, I keep a page on notion that’s public for people to that, you know, had these type of questions on resources, I can just ping them. JS: Great. That’s great. So, yeah, so ping Manuel on Twitter. Twitter, okay? ML: Yeah. That’d be great. JS: So there’s a few other questions here, and I’ll just let people just unmute themselves. So we’ll start with Adida, and again, excuse me if I’m pronouncing names wrong. So if you want to unmute yourself and just ask Manuel yourself, we can just start there. And we could start, start sort of start a list, so. Female Speaker: Cool. Thank you. Hi, Manuel. [0:36:47 inaudible] from Germany. I totally feel your need of collecting historical examples of getting into crazy walk through the centuries to see how much data visualization is actually in our genes. I, myself am a cartographer, and facing now the challenge of trying to organize those wonderful historical examples of storytelling maps. And I was wondering, do you have some sort of a peer to peer tip how do you organize your collections of graphics of trees, networks, circles and so on and so on? That would be great. ML: Yeah. That’s a great point. Well, thank you. It’s always good to meet a kindred spirit. So thanks for that. Yeah. I think a lot more taxonomies especially of these old material are needed, right? And I think we need more people like you to investigate that. So I really appreciate what you’re doing. So the tools that I used, it was, it was hard. I think it was a mix. So I can also send you the list of institutions and many of them not only, like illustrations and I actually thought about doing a blog post at some point, which basically is, again, that the list, the list of institutions are making that collections open and freely available for researchers like, like yourself and myself to use and browse, right? So I think it’s something I’m probably going to do at some point and share with the world and community. But that’s a good question. So there’s a bunch of institutions, and I’m happy to like recommend you a few. The Library of Congress is always the easiest in the sense that it’s public record; it’s in the public domain. So anything that’s there it’s safe to use. There’s a many more in, in Europe, of course, institutions, especially tied with universities. There’s a bunch I can send you that list if you want, if you, if you’re interested. In the process of itself that I used, I actually used Pinterest, believe it or not. I used Pinterest to make sense of some of these images I was uncovering and to discover the patterns, right, the themes that I was uncovering, because sometimes A lot of themes and this is, you know, me looking back at, let me just show you here for a moment. So if, if you haven’t seen one of my books, they always start off, we have them start with a taxonomy in the very first page of this. This is the taxonomy of the Book of Circles, right? And these taxonomy is really hard. These taxonomy doesn’t, it’s not in my mind when I submit the proposal at all, right? It’s, it’s a taxonomy that is that emerges through the research, through the research that I’m doing, right? Collecting all these examples, try to put them in groups and categories, and the taxonomy kind of emerges naturally through that process. And for that process, I use Pinterest a lot to actually start making sense of similarities, resemblances between some of the motifs and some of the styles that people were using. And then I also use illustrator, Adobe Illustrator, just, you know, just an application that allows you to draw, but I would be basically collecting all these images and putting them in a really massive digital whiteboard of sorts, right? And again, grouping them in, in ways that makes sense. And then the great thing about doing a taxonomy like that for the book, and again, I’m just going to show it again, is that it is both the taxonomy of all the types in this case are the seven families of 21 topologies of circular diagrams, right? But it also is a way for you to, it’s also a table of contents, right? It also has a link to the number where each category starts. So you can jump into a specific category in the book, right? So that’s why I think, you know, taxonomies are really important, not just from a research contribution, but also even in a book like that, it allows you to actually understand the whole practice through it. JS: Yeah. That’s really interesting. So Francesca has a question/comment for you. So, um, which I think is a really good one about, about talking the data viz language and reading the viz language, so, okay, there she is. ML: Yeah. Francesca: Hello. Hi, Manuel and everybody. Yeah, so I use, thank you. I mean, I use your book in, at school quite, quite widely. I teach from data visualization design curriculum, from a very graphic point of view. So they’re very, very useful instrument. And I want to share a comment with everybody which is like we, we say in classroom that you can learn how to create data visualizations, but then you also need to learn how to read data visualization, and because there’s a language like the capacity of talking and listening need to be developed, like along one another. Otherwise, like you’re not really talking to anybody, right? So I appreciate the books, because I think that they, they slow down the process, and they really requires, I mean, you can also go through very quickly and just they’re just like beautiful, but if you want to understand them, they slow down the process, and they demand time for the understanding and the listening, which is something that I feel digital tools doesn’t require so explicitly. So if I think about even in the last several weeks, all of the news feed are absolutely bombed by so many visualization kinds, so many charts, so many things and lines that grows and decrease. And I’m not sure what is the, what normal people, ordinary people really understand out of that, because I not sure they have the instrument to actually receive so much information packed and processed in such a condensed language. So, well, this is the comment. If I can have a question is more like how do you think about the capacity of people of reading visualization nowadays? So how much the first piece of the production of this visualization gets along with the capacity, the speed of people to being able to reading them? And what we can do as designers or educators or, or just people that is like visual communicator to help this capacity of reading to increase, you know, to make, to make these people, these tools really useful for the people that received them. ML: That’s, that’s great. I mean, I love your, your connection with language and, and maybe not coincidentally, my webinar next week, which, again, I hope to do it again. And hopefully, when I do the second series, you can join that one, Francesca, is called the Language of Data Visualization. And I kind of start the webinar by this, this hypothetical scenario that, you know, tomorrow you’re going to face an alien, right, that comes and comes to earth and asks you what a cat is, like the animal. And then you try to explain by using a series of descriptors, you know, it’s loud. It’s, it’s this or that you can try to explain a predator. You try to explain what the cat is. Now the understanding of those descriptors would only make sense for the alien if it understands two things, the building blocks of that language, right, of that alphabet. So the letters that you actually apply, which, by the way, are just one single alphabet, and I think, more than 200 known alphabets today live, living off of that we have on planet Earth would only make sense if that, if that alien knows what those building blocks are, the letters of that those words and the grammar or the rules on how to combine that, right. Without those two things, it would be meaningless to explain, and it’s the same thing with graphics. Graphics only makes sense if people understand the building blocks of those graphics, right, mostly conveyed through shapes, color, size, and position, right, things of that nature, the visual variables, and the rules on how to combine that grammar of graphics. It’s the same process, and I go through the webinar, like, what does that mean the visual decoding out the various nuances of that grammar. And, and there’s, but again, answering that it’s all, it has to be through education. And, and I think one of the great things that, for example, John has done, which I love, it’s called the graphic continuum. And I’m sure you can also you should post here as well. It’s one of many examples. I think there’s a few other frameworks. But that one specifically, we actually have the poster version. John, I should have mentioned this in the beginning. We have the poster version at Google. And we have the little one of the desktop version as well. And it’s super useful because again, it allows you to understand it’s a connection between, you know, what type of data you have, what do you want to achieve with that data, and the suggested chart, right? And I think it’s a really like it’s a mental tool. It’s a mental process, and you have to like practice this. There’s a little bit of like language, right? Like I have my five year old Chloe here. And she’s just learning the language, the written language, right? The building blocks, the letters, and how to combine them. And you can see the brain adapting to this new knowledge, right? With graphic language, it’s the same thing. And we cannot expect people to just get it and understand it. Because imagine how long it took you or any of us here to learn written language and to master it right to the point where we’re really comfortable with it. It takes years, right? And now all of a sudden, we are expecting that we can put in front of, in front of people some really complex charts and then just expect them that they would just understand it. It doesn’t happen that way, right? So I think it’s our job, especially your job, Francesca, as well as as an educator to really teach this because the nuances of that language are really important for people to understand, especially as, as an example we are more and more relying on charts as we now see with, of course, the Coronavirus epidemic. And now it’s, it’s, it’s becoming really ubiquitous than the amount of charts. Also because we have a bias of accuracy when we actually see a chart, we perceive that to be more accurate. And it’s, it’s a bias as many biases that we have. And, and actually my, one of my favorite Wikipedia pages is the list of cognitive biases. It’s a great page. And if you are a designer or anyone dealing with data visualization should go to that page, because you really understand that’s the genesis of a lot of principles we used for communicating information and data, right? So one of them is that bias that exists. So if that bias is very prevalent across populations, and if they’re not educated, right, or not to reading and interpreting charts, it’s a problem. Right? There’s a lot of possibility for misunderstanding and misinterpretation. JS: Yeah. Francesca: Thank you. Thank you very much. JS: Yeah. That was, that was great, great question. Great, great discussion. Um, Oksana has a question on learning code. I don’t know if she wants to. ML: Uh, yeah. Should designers UX graphic code in your opinion? Again, it really depends on what you want to achieve, Oksana. It really does. I don’t think it’s a, it’s a need per se, you know, you can easily partner with someone else. I think to be honest, it sometimes and I feel this also as UX manager and, you know, being familiar with some of the startups, I think designers are being asked to do too much these days, you know, they are being asked to do research. They are being asked to do code and are expected to do all these things equally well and achieve, you know, great success and impact on all those things together. I think it’s a lot to ask. And it really takes the emphasis on doing one thing really well, right? And I think no matter what people tell me about and try to sell me the concept of unicorns, like I’ve never met a person like that. Maybe I’ve met actually a couple, but those are really, really hard to find. And these people might not be super happy with their work life balance either, right? So I think you better, my advice is like, yes, I would say, if you want to learn code, I think there’s nothing that you shouldn’t do. I think it’s always worth it to just explore something new. You will learn a new skill set that can only bring good things to you on a personal basis, right? It will challenge you. And it would actually allow you to expand your mind in different ways. But I don’t think that’s a nice like no one will hire you expecting that, that skill if you are being hired as a designer, right? So I don’t think there’s a niche on the market for that necessarily. I think it’s more of a personal option. Like, for example, I did a lot of coding in the past to do some of the projects that I did, but I think no, I’m unable to be as good as coding as I am in other things. So I would rather partner with someone that’s really good at coding and I can focus on other things and through that collaboration we can achieve better, better projects all together. JS: Just to say, I think that’s a great answer, because I agree. I feel like everybody, everybody wants to be a unicorn. And a lot of people who hire want unicorns and neither of those things are, they’re called unicorns for a reason, because they don’t exist. ML: Exactly, don’t exist. JS: Right. I mean, and I would, I would also extend that just, just as sort of a, as an aside, that especially in the moment that we’re in with all these visualizations coming out about COVID and the pandemic, there’s a lot of people creating visualizations who maybe they should not be creating visualizations because they don’t know enough about the spread of a disease and public health and epidemiology. And maybe it’s just human nature where we kind of think that we know more than we do, and instead we should be asking for help. And this, this theme has come up a bunch of times on these discussions and in a podcast I recently did, but I think it’s, it’s not just on what we expect people to do in their job with the tools, but also what we expect people to sort of publish and put out into the world. There seems to be this expectation of like doing too much. And maybe we should be relying on each other a little bit more [0:51:14 crosstalk]. ML: Yeah, I totally agree. And I think to be honest, like the whole idea of unicorns or like, the notion of product designers, which is something that turned out, as you know, according to the whole startup community, to be honest, I think it was just a cheap way of startups to get what they needed for a really low price, right? JS: Yeah. ML: I mean, yes. If I need to hire like a researcher, designer and a software engineer, I would have to pay three times. If I got one person that does all three, you know, that’s great. But I don’t think that should be the guiding force for, you know, any of you to like lead your life through by how to do to save money, if anything. JS: I was, I was just thinking I got in trouble for making this exact case at a conference, at one of the academic viz conferences. This is several years ago where I said, you know, you need to have a team, right? You need someone who can do the web development. You need someone who can do the statistics and the data. You need someone who, you know, can do the design. You need all these, all these different pieces, and there are no unicorns. And I got all these computer science graduate students after the talk saying I can do this, you know, I’m the unicorn. I’m like, yeah, because you’re in graduate school right now. But like what happens when you go to work and you have 500 competing things to do today? You just, it’s just not, it’s just not a tenure, you know, it’s just not something that’s gonna, gonna work for anything. ML: Yeah. That’s agreed. I mean, at the same time, I think it’s definitely useful. Like, especially if you work in a trial like that, you know, if you are designer and you’re working, you know, side by side with engineers, and product managers and other roles and researchers and whatnot. It’s always good to, of course, to understand their roles. You don’t have to be an expert in research or an expert in let’s say software development, but like you need to understand the basics of it so that you know the limitations, you know what they struggle with, right? But that is to say about everything. And you know that there’s always a lot of, a lot of, a lot of aspects that, you know, we designers have to explain what we do so that people understand, like, I think engineers and researchers also need to explain to designers what they do, so that everyone is on the same level playing field. Even internally, I remember doing that, like every time there’s like a new project, a new collaborative project, the best way to start a project is by everyone explaining what they do, what they bring to the table, right? And how to best collaborate with them. That will just clear the water immediately, because now you know what you can rely on that person for, right? And it’s much clearer because instead, there’s a lot of misunderstandings that people aren’t gonna understand what role, what kind of contribution they can do, how to collaborate with them. It’s just avoiding a lot of that process for sure. JS: Yeah. That’s great. Um, we’re basically at the end. I don’t know, do you have any like last words of wisdom for folks that you want to share or I don’t know. ML: Not really. I just hope everyone is safe. And, and I’m happy that you’re doing this, John. I think it’s really good. And I think, Francesca before, like I think all of us doing and trying to educate the public on some of these issues, right. I think it can only bring good things, right. And I think, you know, visual literacy is definitely an issue. And I think we all collectively need to work on it in different ways. It’s through, you know, initiatives like this one, through webinars, through whatever educational platforms we can create. I think, it’s a really like important call to action for us to all be involved in. JS: Yeah, that’s great. I’ll just quickly before we close up, just quickly remind everybody that there are, I don’t even know what it’s, Wednesday. It’s like the longest month ever. So, tomorrow, we’ll do, I don’t even remember what time we’re at, but tomorrow, let’s see. Especially for people interested in tools from 10 to 11 AM, Eastern Time, Gregor Irish and Lisa Charlotte Rost from Data Wrapper. They’re in Berlin. They run, they are part of the team on Datawrapper. So they’ll be here to talk about the tool and other stuff. And then Friday afternoon from two to three Eastern Time Enrico Bertini and Moritz Stefaner are from the Data Stories podcast would be here to talk about the stuff that they’re working on. And then just as a quick like a preview for next week, I have four of the five days all set, but on Wednesday, I’ll be doing this on my own. And I’ll be teaching data viz for kids. So if you have kids, Manuel, you mentioned this about your daughter earlier, so– ML: It’s awesome. Yeah. JS: Yeah. So I’m going to try to do a little virtual data viz class for kids. So all your kid needs or you doesn’t matter, right, they just need a piece of paper and some colored pencils. And we’re going to, I’ll talk for a little bit, and then we’ll actually be making some things, so, but I’ll be– ML: That’s amazing. Can, can I don’t want to join as well? JS: Of course, of course. It’s totally open. So, yeah. ML: What time is that, John? JS: I, well, you know what, I was just thinking of it. And I think I’m gonna do it at 11 because you said 11 is a good time. So I think I’ll do at 11 because I think about kids on the East Coast in San Francisco, they’re probably up early anyways. So maybe their parents can say go be online for an hour. So we’ll do 11 AM on Wednesday and, and then we can get out to, out to Pakistan at least. ML: That sounds great. JS: Great. ML: The 11 is good because it opens appetite for lunch as well. JS: Right. That’s right. That’s right. Yeah. All right, everybody. Well, thanks so much for, for coming on. Manuel, thanks so much. This was a lot of fun. It’s great. Be sure to check out his books. And I think if you have other questions, just ping him on Twitter. And if you have any questions for me, ping me on Twitter, or the Urban website, wherever and we can share more resources. So, great. Everybody, stay safe, stay healthy, and be well, and we’ll see you soon. Thanks a lot. Everybody. See you Manuel. ML: Thanks to everyone. JS: Thanks for everyone for tuning in to this week’s episode of the podcast. I hope you enjoyed that. I hope you learned a little something about Manuel’s process and about his love of circles and his love of trees. If you’d like to support the podcast, please consider sharing it, letting other folks know about the show. Consider writing a review on iTunes, Spotify, or your favorite podcast provider. Or if you’d like to support the show financially, please head over to my Patreon page for just a couple of dollars per month. You can help me afford things like audio transcription, audio editing, webcasting, web hosting, all the things that are needed to bring this show to you. So I hope you are well. I hope you are safe. And until next time, this has been the PolicyViz podcast. Thanks so much for listening. The post Episode #176: Manuel Lima appeared first on Policy Viz.
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