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METR · June 27, 2025

METR proposes evidence questions for frontier-model risk disclosure

analysis AI ComplianceModel Evaluation
Why it matters

METR’s June 2025 proposal asks what AI developers should disclose about serious model risks, including internal capabilities, monitoring, safety investigations and incident history. It organizes questions around information that outsiders cannot reliably infer from public model behavior, and discusses disclosure incentives and the limits of developer self-reporting. Sensitive evidence could be assessed by trusted reviewers rather than exposed publicly. This is a proposed transparency framework, not a tested assurance standard or legal compliance checklist. Its practical value is turning broad safety statements into specific requests for evidence and accountable review.

My takeaway: Attach an evidence owner, review date and disclosure audience to each risk question. Record unanswered questions and independent verification needs rather than treating a completed questionnaire as proof of safety.
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