METR proposes a plan to better inform the public about the risk of losing control of frontier AI development.
A proposal for better informing the public about loss-of-control risks from frontier AI development
METR released a proposal advocating that independent evaluators provide the public with evidence on whether frontier AI developers might lose control of their internal AI systems. The proposal lists three questions needing more public evidence: model alignment, whether monitoring and control measures can effectively intercept, and capability acceleration from AI R&D automation, mentioning that Anthropic says its codebase change speed has increased 10x in recent months. METR suggests a risk-assessment exercise format where evaluators access internal datasets and systems on company devices and privately interview employees, after which METR writes a public report, with companies able to redact sensitive information. METR states the proposal is voluntary, does not cover safety audits such as model weight security, and does not involve pre-deployment model evaluation, and cannot provide audit- or regulatory-level assurance.
METR proposes a reporting framework for independent evaluators to enter companies for loss-of-control risk assessment, listing three evidence gaps—alignment, control, and AI R&D acceleration—and access conditions.