102. Wendy Foster - AI ethics as a user experience challenge

Towards Data Science

11/10/21

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44m

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AI ethics is often treated as a dry, abstract academic subject. It doesn’t have the kinds of consistent, unifying principles that you might expect from a quantitative discipline like computer science or physics.

But somehow, the ethics rubber has to meet the AI road, and where that happens — where real developers have to deal with real users and apply concrete ethical principles — is where you find some of the most interesting, practical thinking on the topic.

That’s why I wanted to speak with Wendy Foster, the Director of Engineering and Data Science at Shopify. Wendy’s approach to AI ethics is refreshingly concrete and actionable. And unlike more abstract approaches, it’s based on clear principles like user empowerment: the idea that you should avoid forcing users to make particular decisions, and instead design user interfaces that frame AI-recommended actions as suggestions that can be ignored or acted on.

Wendy joined me to discuss her practical perspective on AI ethics, the importance of user experience design for AI products, and how responsible AI gets baked into product at Shopify on this episode of the TDS podcast.

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Intro music:

Artist: Ron Gelinas

Track Title: Daybreak Chill Blend (original mix)

Link to Track: https://youtu.be/d8Y2sKIgFWc

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Chapters:

0:00 Intro

1:40 Wendy’s background

4:40 What does practice mean?

14:00 Different levels of explanation

19:05 Trusting the system

24:00 Training new folks

30:02 Company culture

34:10 The core of AI ethics

40:10 Communicating with the user

44:15 Wrap-up

Previous Episode

101. Ayanna Howard - AI and the trust problem

November 3, 2021

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53m

Over the last two years, the capabilities of AI systems have exploded. AlphaFold2, MuZero, CLIP, DALLE, GPT-3 and many other models have extended the reach of AI to new problem classes. There’s a lot to be excited about.

But as we’ve seen in other episodes of the podcast, there’s a lot more to getting value from an AI system than jacking up its capabilities. And increasingly, one of these additional missing factors is becoming trust. You can make all the powerful AIs you want, but if no one trusts their output — or if people trust it when they shouldn’t — you can end up doing more harm than good.

That’s why we invited Ayanna Howard on the podcast. Ayanna is a roboticist, entrepreneur and Dean of the College of Engineering at Ohio State University, where she focuses her research on human-machine interactions and the factors that go into building human trust in AI systems. She joined me to talk about her research, its applications in medicine and education, and the future of human-machine trust.

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Intro music:

Artist: Ron Gelinas

Track Title: Daybreak Chill Blend (original mix)

Link to Track: https://youtu.be/d8Y2sKIgFWc

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Chapters:

0:00 Intro

1:30 Ayanna’s background

6:10 The interpretability of neural networks

12:40 Domain of machine-human interaction

17:00 The issue of preference

20:50 Gelman/newspaper amnesia

26:35 Assessing a person’s persuadability

31:40 Doctors and new technology

36:00 Responsibility and accountability

43:15 The social pressure aspect

47:15 Is Ayanna optimistic?

53:00 Wrap-up

Next Episode

103. Gillian Hadfield - How to create explainable AI regulations that actually make sense

November 17, 2021

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51m

It’s no secret that governments around the world are struggling to come up with effective policies to address the risks and opportunities that AI presents. And there are many reasons why that’s happening: many people — including technical people — think they understand what frontier AI looks like, but very few actually do, and even fewer are interested in applying their understanding in a government context, where salaries are low and stock compensation doesn’t even exist.

So there’s a critical policy-technical gap that needs bridging, and failing to address that gap isn’t really an option: it would mean flying blind through the most important test of technological governance the world has ever faced. Unfortunately, policymakers have had to move ahead with regulating and legislating with that dangerous knowledge gap in place, and the result has been less-than-stellar: widely criticized definitions of privacy and explainability, and definitions of AI that create exploitable loopholes are among some of the more concerning results.

Enter Gillian Hadfield, a Professor of Law and Professor of Strategic Management and Director of the Schwartz Reisman Institute for Technology and Society. Gillian’s background is in law and economics, which has led her to AI policy, and definitional problems with recent and emerging regulations on AI and privacy. But — as I discovered during the podcast — she also happens to be related to Dyllan Hadfield-Menell, an AI alignment researcher whom we’ve had on the show before. Partly through Dyllan, Gillian has also been exploring how principles of AI alignment research can be applied to AI policy, and to contract law. Gillian joined me to talk about all that and more on this episode of the podcast.

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Intro music:

Artist: Ron Gelinas

Track Title: Daybreak Chill Blend (original mix)

Link to Track: https://youtu.be/d8Y2sKIgFWc

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