December 31, 2019

029 - Why Google Believes it’s Critical to Pair Designers with Your Data Scientists to Produce Human-Centered ML & AI Products with Di Dang

45 minutes

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 common problem that we hear. So then, machine learning becomes this hammer for which everything is a nail—but if only a hammer were as easy to construct as a piece of wood and a little metal anvil kind of bit.” — Di “A lot of folks are still evolving their own mental model around what machine learning is and what it's good for. But closely in relation—because this is something that I thin

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By Brian T. O’Neill from Designing for Analytics

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