Harmonizing AI Safety Thresholds

2026-07-17

AI Summary

This study addresses the absence of standardized safety capability thresholds among leading AI developers, which hinders third-party verification and comparison and risks a race to the bottom in safety standards. The work proposes the first coordinated threshold framework spanning three risk domains: cyber misuse, biological misuse, and autonomous AI development. For misuse risks, the framework centers on expected harm, integrating risk pathway analysis with release condition modeling; for autonomous development risks, it dynamically assesses thresholds based on the pace of AI progress. By differentiating evaluation logics across risk domains, the framework addresses gaps in existing empirical research and establishes a unified, comparable, and verifiable system of AI safety thresholds, offering a methodological foundation for regulatory and industry standards.

Abstract

Frontier AI companies have published capability thresholds that differ substantially, making it difficult for third parties to verify whether a threshold has been crossed or to compare requirements across companies. Moreover, without common minimum thresholds, risk mitigation may be inconsistent, creating a potential race to the bottom in safety standards. We develop a methodology for deriving harmonized thresholds across three risk domains. For misuse risks (cyber and biological), we take expected harm as the key primitive and use an explicit risk-modeling approach that accounts for risk channels and model release conditions. For automated AI R&D, we base our proposed threshold on the observed rate of AI progress rather than expected harm. Our analysis expands upon prior work and highlights existing empirical gaps and limitations.

Problem

• AI safety thresholds
• harmonization
• misuse risks
• automated AI R&D
• risk mitigation

Innovation

• harmonized thresholds
• expected harm
• risk modeling
• AI safety
• automated AI R&D