103. Gillian Hadfield - How to create explainable AI regulations that actually make sense
Towards Data Science
11/17/21
•51m
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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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Chapters:
- 1:35 Gillian’s background
- 8:44 Layers and governments’ legislation
- 13:45 Explanations and justifications
- 17:30 Explainable humans
- 24:40 Goodhart’s Law
- 29:10 Bringing in AI alignment
- 38:00 GDPR
- 42:00 Involving technical folks
- 49:20 Wrap-up
Previous Episode
102. Wendy Foster - AI ethics as a user experience challenge
November 10, 2021
•44m
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
---
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
Next Episode
104. Ken Stanley - AI without objectives
November 24, 2021
•66m
Today, most machine learning algorithms use the same paradigm: set an objective, and train an agent, a neural net, or a classical model to perform well against that objective. That approach has given good results: these types of AI can hear, speak, write, read, draw, drive and more.
But they’re also inherently limited: because they optimize for objectives that seem interesting to humans, they often avoid regions of parameter space that are valuable, but that don’t immediately seem interesting to human beings, or the objective functions we set. That poses a challenge for researchers like Ken Stanley, whose goal is to build broadly superintelligent AIs — intelligent systems that outperform humans at a wide range of tasks. Among other things, Ken is a former startup founder and AI researcher, whose career has included work in academia, at UberAI labs, and most recently at OpenAI, where he leads the open-ended learning team.
Ken joined me to talk about his 2015 book Greatness Cannot Be Planned: The Myth of the Objective, what open-endedness could mean for humanity, the future of intelligence, and even AI safety on this episode of the TDS podcast.
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