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
124 - The PiCAA Framework: My Method to Generate ML/AI Use Cases from a UX Perspective
123 - Learnings From the CDOIQ Symposium and How Data Product Definitions are Evolving with Brian T. O’Neill
122 - Listener Questions Answered: Conducting Effective Discovery for Data Products with Brian T. O’Neill
121 - How Sainsbury’s Head of Data Products for Analytics and ML Designs for User Adoption with Peter Everill
120 - The Portfolio Mindset: Data Product Management and Design with Nadiem von Heydebrand (Part 2)
119 - Skills vs. Roles: Data Product Management and Design with Nadiem von Heydebrand (Part 1)
118 - Attracting Talent and Landing a Role in Data Product Management with Kyle Winterbottom
117 - Phil Harvey, Co-Author of “Data: A Guide to Humans,” on the Non-Technical Skills Needed to Produce Valuable AI Solutions
116 - 10 Reasons Your Customers Don’t Make Time for Your Data Product Initiatives + A Big Update on the Data Product Leadership Community (DPLC)
115 - Applying a Product and UX-Driven Approach to Building Stuart’s Data Platform with Osian Jones
114 - Designing Anti-Biasing and Explainability Tools for Data Scientists Creating ML Models with Josh Noble
113 - Turning the Weather into an Indispensable Data Product for Businesses with Cole Swain, VP Product at tomorrow.io
112 - Solving for Common Pitfalls When Developing a Data Strategy featuring Samir Sharma, CEO of datazuum
111 - Designing and Monetizing Data Products Like a Startup with Yuval Gonczarowski
110 - CDO Spotlight: The Value and Journey of Implementing a Data Product Mindset with Sebastian Klapdor of Vista
109 - The Role of Product Management and Design in Turning ML/AI into a Valuable Business with Bob Mason from Argon Ventures
108 - Google Cloud’s Bruno Aziza on What Makes a Good Customer-Obsessed Data Product Manager
107 - Tom Davenport on Data Product Management and the Impact of a Product Orientation on Enterprise Data Science and ML Initiatives
106 - Ideaflow: Applying the Practice of Design and Innovation to Internal Data Products w/ Jeremy Utley
105 - Defining “Data Product” the Producty Way and the Non-technical Skills ML/AI Product Managers Need
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