Response to U.S. AI Safety Institute’s Request for Comment on Managing Misuse Risk For Dual-Use Foundation Models
Stanford scholars respond to a federal RFC on the U.S. AI Safety Institute’s draft guidelines for managing the misuse risk for dual-use foundation models.
In collaboration with
Executive Summary
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.
Related Publications
Designing Loyalty: AI Agents and Conflicts of Interest
This brief examines the conflict-of-interest risks posed by AI agents and calls for imposing a duty of loyalty on developers and deployers.
This brief examines the conflict-of-interest risks posed by AI agents and calls for imposing a duty of loyalty on developers and deployers.
Regulating Data Brokers in the Age of AI: A California Case Study
This brief assesses data broker compliance with California privacy laws and calls for more stringent consumer protections.
This brief assesses data broker compliance with California privacy laws and calls for more stringent consumer protections.
This brief highlights the emergence of world models and outlines a first-of-its-kind governance and policy agenda for the technology.
This brief highlights the emergence of world models and outlines a first-of-its-kind governance and policy agenda for the technology.
Operationalizing Real-Time Monitoring of Clinical AI
This brief demonstrates how real-time monitoring can address critical gaps in the oversight of radiological AI tools.
This brief demonstrates how real-time monitoring can address critical gaps in the oversight of radiological AI tools.