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
044 - The Roles of Product and Design when “Competing in the Age of AI” with HBS Professor and Author Karim Lakhani
043 - What a Product Management Mindset Can do for Data Science and Analytics Leaders with Product School CEO, Carlos González de Villaumbrosia
042 - Why Machine Learning and Analytics Alone Can’t Drive Behavioral Change inside Police Departments with Allison Weil
041 - Data Thinking: An Approach to Using Design Thinking to Maximize the Effectiveness of Data Science and Analytics with Martin Szugat of Datentreib...
040 – Improving Potato Chips and Space Travel: NASA’s Steve Rader on Open Innovation
039 – How PEX Fingerprinted 20 Billion Audio and Video Files and Turned It Into a Product to Help Musicians, Artists and Creators Monetize their Work
038 – (Special Co-Hosted Episode) Brian and Mark Bailey Discuss 10 New Design and UX Considerations for Creating ML and AI-Driven Products and Applica...
037 – A VC Perspective on AI and Building New Businesses Using Machine Intelligence featuring Rob May of PJC
036 – How Higher-Ed Institutions are Using AI and Analytics to Better Serve Students with Professor of Learning Informatics and Edtech Expert Simon Bu...
035 – Future Ethics Author and Designer Cennydd Bowles Shares Strategies for Designing Ethical Data Products That Benefit Our Business, Community and ...
034 – ML & UX: To Augment or Automate? Plus, Rating Overall Analytics Efficacy with Eric Siegel, Ph.D.
033 - How Vidant Health’s Data Team Creates Empathetic Data Products and Ethical Machine Learning Models with Greg Nelson
032 - How and Why Talented Analytical Minds Leave People Scratching Their Head Around Data with Nancy Duarte
031 - How Design Helps Enable Repeatable Value on AI, ML, and Analytics Projects with Ganes Kesari
030 - Using AI to Recommend Personalized Medical Treatment Options with Joost Zeeuw of Pacmed
028 - Cole Nussbaumer Knaflic On Data Storytelling, DataViz, and Why Your Data May Not Be Inspiring Action
027 - Balancing Your Inner Data Science Nerd While Becoming a Trusted Business Advisor and Strategist with Angela Bassa of iRobot
026 - Why Tom Davenport Gives a 2 out of 10 Score To the Data Science and Analytics Industry for Value Creation
025 - Treating Data Science at IDEO as a Discipline of Design with Dean Malmgren
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