Innovating AI Regulation: The Need for Proofs of Concept and Market Involvement

This chapter focuses on the necessity of creating proof of concepts for effective AI regulation and governance. The speakers emphasize innovation and collaboration in refining regulatory approaches while advocating for government oversight to manage associated risks.

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Dr. Gillian Hadfield from Johns Hopkins University and Andrew Freedman from Fathom discuss their innovative proposal to govern AI through private regulatory markets, which has been introduced as California's SB 813. Their system would separate democratic goal-setting from technical rule-making by having government bodies articulate safety outcomes while competitive private certifiers develop and enforce detailed standards, with companies receiving liability protection for compliance. The conversation explores how this market-based approach could create a "race to the top" in AI safety standards while remaining agile enough to keep pace with rapid technological development. Key challenges discussed include preventing a race to the bottom among certifiers, liability law interactions, and identifying qualified organizations to serve as effective private regulators.

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CHAPTERS:

(00:00) About the Episode

(04:48) Introduction and Problem Overview

(07:48) Regulatory Markets Concept Origins

(17:14) Current Governance System Failures (Part 1)

(19:28) Sponsors: Fin | Labelbox

(22:42) Current Governance System Failures (Part 2)

(25:30) Private Governance Mechanism Explained (Part 1)

(35:06) Sponsors: Oracle Cloud Infrastructure | NetSuite by Oracle

(37:38) Private Governance Mechanism Explained (Part 2)