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It\\u2019s 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\\u2019s happening: many people \\u2014 including technical people \\u2014 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\\u2019t even exist.

So there\\u2019s a critical policy-technical gap that needs bridging, and failing to address that gap isn\\u2019t 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\\u2019s 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 \\u2014 as I discovered during the podcast \\u2014 she also happens to be related to Dyllan Hadfield-Menell, an AI alignment researcher whom we\\u2019ve 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:

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• 1:35 Gillian\\u2019s background
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• 8:44 Layers and governments\\u2019 legislation
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• 13:45 Explanations and justifications
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• 17:30 Explainable humans
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• 24:40 Goodhart\\u2019s Law
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• 29:10 Bringing in AI alignment
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• 38:00 GDPR
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• 42:00 Involving technical folks
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• 49:20 Wrap-up
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