The AI Regulatory Alignment Problem
This brief, produced in collaboration with Stanford RegLab, sheds light on the “regulatory misalignment” problem by considering the technical and institutional feasibility of four commonly proposed AI regulatory regimes.
Key Takeaways
Although the demand for AI regulation is at a near fever pitch and may reflect a variety of legitimate concerns, four common proposals to regulate AI—mandatory disclosure, registration, licensing, and auditing regimes—are not the magic remedy to cure all that ails AI. Before rushing into regulation, policymakers should consider feasibility, trade-offs, and unintended consequences.
Many proposals suffer from what we call the “regulatory alignment problem,” where a regulatory regime’s objective or impact either fails to remediate the AI-related risk at issue (i.e., regulatory mismatch) or conflicts with other societal values and regulatory goals (i.e., value conflict).
Establishing an AI super-regulator risks creating redundant, ambiguous, or conflicting jurisdiction given the breadth of AI applications and the number of agencies with existing AI-related regulatory authorities.
Adverse event reporting and third party-audits with government oversight can address key impediments to effective regulation by enabling the government to learn about risks of AI models and verify industry claims without drastically increasing its capacity.
Policymakers should not expect uniform implementation of regulatory principles absent clear guidance given operationalizing high-level definitions (e.g., “dangerous capabilities”) and AI principles (e.g., “fairness”) is not self-evident, value-neutral or even technically feasible in some cases.
Related Publications
In this response to a request for information issued by the National Science Foundation’s Networking and Information Technology Research and Development National Coordination Office (on behalf of the Office of Science and Technology Policy), scholars from Stanford HAI, CRFM, and RegLab urge policymakers to prioritize four areas of policy action in their AI Action Plan: 1) Promote open innovation as a strategic advantage for U.S. competitiveness; 2) Maintain U.S. AI leadership by promoting scientific innovation; 3) Craft evidence-based AI policy that protects Americans without stifling innovation; 4) Empower government leaders with resources and technical expertise to ensure a “whole-of-government” approach to AI governance.
In this response to a request for information issued by the National Science Foundation’s Networking and Information Technology Research and Development National Coordination Office (on behalf of the Office of Science and Technology Policy), scholars from Stanford HAI, CRFM, and RegLab urge policymakers to prioritize four areas of policy action in their AI Action Plan: 1) Promote open innovation as a strategic advantage for U.S. competitiveness; 2) Maintain U.S. AI leadership by promoting scientific innovation; 3) Craft evidence-based AI policy that protects Americans without stifling innovation; 4) Empower government leaders with resources and technical expertise to ensure a “whole-of-government” approach to AI governance.
Safeguarding Third-Party AI Research
This brief examines the barriers to independent AI evaluation and proposes safe harbors to protect good-faith third-party research.
This brief examines the barriers to independent AI evaluation and proposes safe harbors to protect good-faith third-party research.
This white paper assesses federal efforts to advance leadership on AI innovation and governance through recent executive actions and emphasizes the need for senior-level leadership to achieve a whole-of-government approach.
This white paper assesses federal efforts to advance leadership on AI innovation and governance through recent executive actions and emphasizes the need for senior-level leadership to achieve a whole-of-government approach.
In this response to the U.S. AI Safety Institute’s (US AISI) request for comment on its draft guidelines for managing the misuse risk for dual-use foundation models, scholars from Stanford HAI, the Center for Research on Foundation Models (CRFM), and the Regulation, Evaluation, and Governance Lab (RegLab) urge the US AISI to strengthen its guidance on reproducible evaluations and third- party evaluations, as well as clarify guidance on post-deployment monitoring. They also encourage the institute to develop similar guidance for other actors in the foundation model supply chain and for non-misuse risks, while ensuring the continued open release of foundation models absent evidence of marginal risk.
In this response to the U.S. AI Safety Institute’s (US AISI) request for comment on its draft guidelines for managing the misuse risk for dual-use foundation models, scholars from Stanford HAI, the Center for Research on Foundation Models (CRFM), and the Regulation, Evaluation, and Governance Lab (RegLab) urge the US AISI to strengthen its guidance on reproducible evaluations and third- party evaluations, as well as clarify guidance on post-deployment monitoring. They also encourage the institute to develop similar guidance for other actors in the foundation model supply chain and for non-misuse risks, while ensuring the continued open release of foundation models absent evidence of marginal risk.