Harmonizing AI Safety Thresholds
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 ob...
| Authors | Wilber Sean Anterola, Matthew Ball, Luis F. Lafuerza et al. |
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| Primary category | cs.AI |
| Published | 17 July 2026 |
| Last updated at source | 17 July 2026 |
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