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
024 - How Empathy Can Reveal a 60%-Accurate Data Science Solution is a Solid Customer Win with David Stephenson, Ph.D.
023 - Balancing AI-Driven Automation with Human Intervention When Designing Complex Systems with Dr. Murray Cantor
022 - Creating a Trusted Data Science Team That Is Indispensable to the Business with
021 - Turning Complex Cloud IT Data Into Useful Decision Support Info with John Purcell of
020 - How Human-Centered Design Increases Engagement with Data Science Initiatives
019 - The Non-Technical (Human!) Challenges that Can Impede Great Data Science Solutions
018 - The Business Value of Showing the “Why” in AI Models with Jana Eggers (CEO, Naralogics)
017 - John Cutler on Productizing Storytelling Measuring What Matters & Analytics Product Management
016 - Farming with Data: How Advanced Analytics are Transforming the Agriculture Industry with Dinu Ajikutra
015 – Opportunities and Challenges When Designing IoT Analytics Experiences for the Industrial & Manufacturing Industries with CEO Bill Bither
014 - How Worthington Industries Makes Predictive Analytics Useful from the Steel Mill Floor to the Corner Office with Dr. Stephen Bartos
013 - Paul Mattal (Dir. of Network Systems, Akamai) on designing decision support tools and analytics services for the largest CDN on the web
012 - Dr. Andrey Sharapov (Data Scientist, Lidl) on explainable AI and demystifying predictions from machine learning models for better user experienc...
011 - Gadi Oren (VP Product, LogicMonitor) on analytics for monitoring applications and looking at declarative analytics as “opinions”
010 - Carl Hoffman (CEO, Basis Technology) on text analytics, NLP, entity resolution, and why exact match search is stupid
009 - Nancy Hensley (Chief Digital Officer, IBM Analytics) on the role of design and UX in modernizing analytics tools as old as 50 years
008 - Dr. Puneet Batra (Assoc. Director, Machine Learning at Broad Institute of MIT and Harvard) on aligning data science with biz objectives, user re...
007 -Jim Psota (CTO & Co-Founder, Panjiva/S&P Global) on designing a meaningful SAAS analytics product for the global supply chain
006 - Julien Benatar (PM for Pandora's data service, Next Big Sound) on analytics for musicians, record labels and performing artists
005 - Jason Krantz (Dir. of Biz Analytics/Insights, Weil-McClain) on centering analytics around internal customers
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