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Episode Info: Di Dang is an emerging tech design advocate at Google and helped lead the creation of Google’s People + AI Guidebook. In her role, she works with product design teams, external partners, and end users to support the creation of emerging tech experiences. She also teaches a course on immersive technology at the School of Visual Concepts. Prior to these positions, Di worked as an emerging tech lead and senior UX designer at POP, a UX consultant at Kintsugi Creative Solutions, and a business development manager at AppLift. She earned a bachelor of arts degree in philosophy and religious studies from Stanford University. Join Brian and Di as they discuss the intersection of design and human-centered AI and: Why a data science leader should care about design and integrating designers during a machine-learning project, and the impacts when they do not What exactly Di does in her capacity as an emerging tech design advocate at Google and the definition of human-centered AI How design helps data science teams save money and time by elucidating the problem space and user needs The two key purposes of Google’s People + AI Research (PAIR) team What Google’s  triptych methodology is and how it helps teams prevent building the wrong solution A specific example of how user research and design helped ship a Pixel 2 feature How to ensure an AI solution is human-centered when a non-tech company wants to build something but lacks a formal product manager or UX lead/resource The original goals behind the creation of Google’s People + AI Guidebook The role vocabulary plays in human-centered AI design Resources and Links Twitter: @Dqpdang Di Dang’s Website Di Dang on LinkedIn People + AI Guidebook Quotes from Today's Episode “Even within Google, I can't tell you how many times I have tech leaders, engineers who kind of cock an eyebrow at me and ask, ‘Why would design be involved when it comes to working with machine learning?’” — Di “Software applications of machine learning is a relatively nascent space and we have a lot to learn from in terms of designing for it. The People + AI Guidebook is a starting point and we want to understand what works, what doesn't, and what's missing so that we can continue to build best practices around AI product decisions together.” — Di “The key value proposition that design brings is we want to work with you to help make sure that when we're utilizing machine learning, that we're utilizing it to solve a problem for a user in a way that couldn't be done through other technologies or through heuristics or rules-based programming—that we're really using machine learning where it's most needed.” — Di “A key piece that I hear again and again from internal Google product teams and external product teams that I work with is that it's very, very easy for a lot of teams to default to a tech-first kind of mentality. It's like, ‘Oh, well you know, machine learning, should we ML this?’ That's a very comm...
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