Legal Infrastructure for Transformative AI Governance
It tackles the problem of establishing effective governance frameworks for AI, which is crucial for policymakers and stakeholders, but is incremental as it builds on existing governance concepts.
The paper addresses the need for legal and regulatory infrastructure to govern transformative AI, proposing three examples: registration regimes for frontier models and autonomous agents, and regulatory markets for private AI regulatory services.
Most of our AI governance efforts focus on substance: what rules do we want in place? What limits or checks do we want to impose on AI development and deployment? But a key role for law is not only to establish substantive rules but also to establish legal and regulatory infrastructure to generate and implement rules. The transformative nature of AI calls especially for attention to building legal and regulatory frameworks. In this PNAS Perspective piece I review three examples I have proposed: the creation of registration regimes for frontier models; the creation of registration and identification regimes for autonomous agents; and the design of regulatory markets to facilitate a role for private companies to innovate and deliver AI regulatory services.