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
084 - The Messy Truth of Designing and Building a Successful Analytics SAAS Product featuring Jonathan Kay (CEO, Apptopia)
083 -Why Bob Goodman Thinks Product Management and Design Must Dance Together to Create “Experience Layers” for Data Products
082 - What the 2021 $1M Squirrel AI Award Winner Wants You To Know About Designing Interpretable Machine Learning Solutions w/ Cynthia Rudin
081 - The Cultural and $ Benefits of Human-Centered AI in the Enterprise: Digging Into BCG/MIT Sloan’s AI Research w/ François Candelon
080 – How to Measure the Impact of Data Products…and Anything Else with Forecasting and Measurement Expert Doug Hubbard
079 - How Sisu’s CPO, Berit Hoffmann, Is Approaching the Design of Their Analytics Product…and the UX Mistakes She Won’t Make Again
078 - From Data to Product: What is Data Product Management and Why Do We Need It with Eric Weber
077 - Productizing Analytics for Performing Arts Organizations with AMS Analytics CPO Jordan Gross Richmond
076 - How Bedrock’s “Data by Design” Mantra Helps Them Build Human-Centered Solutions with Jesús Templado
075 - How CDW is Integrating Design Into Its Data Science and Analytics Teams with Prasad Vadlamani
074 - Why a Former Microsoft ML/AI Researcher Turned to Design to Create Intelligent Products from Messy Data with Abhay Agarwal, Founder of Polytopal
073 - Addressing the Functional and Emotional Needs of Users When Designing Data Products with Param Venkataraman
072 - How to Get Stakeholders to Reveal What They Really Need From a Data Product with Cindy Dishmey Montgomery
071 - The ROI of UX Research and How It Applies to Data Products with Bill Albert
070 - Fighting Fire with ML, the AI Incident Database, and Why Design Matters in AI-Driven Software with Sean McGregor
069 - The Role of Creativity and Product Thinking in Data Monetization with ‘Infonomics’ Author Doug Laney
068 - Why User Adoption of Enterprise Data Products Continues to Lag with International Institute for Analytics Executive VP Drew Smith
067 - Why Roche Diagnostics’ BI and Data Science Teams Are Adopting Human-Centered Design and UX featuring Omar Khawaja
066 - How Alison Magyari Used Design Thinking to Transform Eaton’s Business Analytics Approach to Creating Data Products
065 - Balancing Human Intuition and Machine Intelligence with Salesforce Director of Product Management Pavan Tumu
Create your
podcast in
minutes
It is Free
Insight Story: Tech Trends Unpacked
Zero-Shot
Fast Forward by Tomorrow Unlocked: Tech past, tech future
The Unbelivable Truth - Series 1 - 26 including specials and pilot
Lex Fridman Podcast