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:
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 think people don’t talk as much about maybe because it’s less sexy to talk about than machine learning—is that there are often times a lot of organizational or political or cultural uncertainties or confusion around even integrating machine learning.” — Di
“I think there’s a valid promise that there’s a real opportunity with AI. It’s going to change businesses in a significant way and there’s something to that. At the same time, it’s like go purchase some data scientists, throw them in your team, and have them start whacking stuff. And they’re kind of waiting for someone to hand them a good problem to work on and the business doesn’t know and they’re just saying, ‘What is our machine learning strategy?’ And so someone in theory hopefully is hunting for a good problem to solve.” — Brian
“Everyone’s trying to move fast all the time and ship code and a lot of times we focus on the shipping of code and the putting of models into production as our measurement—as opposed to the outcomes that come from putting something into production.” — Brian
“The difference between the good and the great designer is the ability to merge the business objectives with ethically sound user-facing and user-centered principles.” — Brian
065 - Balancing Human Intuition and Machine Intelligence with Salesforce Director of Product Management Pavan Tumu
064 - How AI Shapes the Products of Startups in MIT’s “Tough Tech” Venture Fund, The Engine feat. General Partner, Reed Sturtevant
063 - Beyond Compliance: Designing Data Products With Data Privacy As a UX Benefit with The Data Diva (Debbie Reynolds)
062 - Why Ben Shneiderman is Writing a Book on the Importance of Designing Human-Centered AI
061 - Applying a Product Mindset to Internal Data Products with Silicon Valley Product Group Partner Marty Cagan
060 - How NPR Uses Data to Drive Editorial Decisions in the Newsroom with Sr. Dir. of Audience Insights Steve Mulder
059 - How Design Thinking Helps Organizations and Data Science Teams Create Economic Value with Machine Learning and Analytics feat. Bill Schmarzo
058 - IoT Spotlight: 8 UI / UX Strategies for Designing Indispensable Monitoring Applications
057 - How to Design Successful Enterprise Data Products When You Have Multiple User Types to Satisfy
056 - How Design Helps Drive Adoption of Data Products Used for Social Work with Chief Data Officer Dr. Besa Bauta of MercyFirst
055 - What Can Carol Smith’s Ethical AI Work at the DoD Teach Us About Designing Human-Machine Experiences?
054 - Jared Spool on Designing Innovative ML/AI and Analytics User Experiences
053 - Creating (and Debugging) Successful Data Product Teams with Jesse Anderson
052 - Reasons Automated Decision Making with Machine Learning Can Fail with James Taylor
051 - Methods for Designing Ethical, Human-Centered AI with Undock Head of Machine Learning, Chenda Bunkasem
050 - Ways to Practice Creativity and Foster Innovation When You’re An Analytical Thinker
049 - CxO & Digital Transformation Focus: (10) Reasons Users Can’t or Won’t Use Your Team’s ML/AI-Driven Software and Analytics Applications
048 - Good vs. Great: (10) Things that Distinguish the Best Leaders of Intelligent Products, Analytics Applications, and Decision Support Tools
047 - How Yelp Integrates Data Science, Engineering, UX, and Product Management when Creating AI Products with Yelp’s Justin Norman
046 - How Steelcase’s Data Science, UX, & Product Teams Are Helping Customers Design Safer Office Workplaces Informed by Covid-19 Recommendations w/ J...
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