What AI Red-Team Evaluations Can and Cannot Prove
Red-team evaluations of AI models support some claims and not others, and the boundary between the two is calculable rather than merely a matter of judgment. We define the evidential ceiling of an evaluation as the largest factor by which one result can move belief under a fixed testing budget, derive it in closed form for the benchmark null result, and use it to locate that boundary exactly. We find that above a calculable harm rate, a benchmark of modest size certifies a category to a stated evidentiary standard, and a clean sheet is then the stronger of the two possible observations, outweighing a single reproduced failure. Below that rate, no passive benchmark of feasible size provide...
| Authors | Bandana Kaur |
|---|---|
| Primary category | cs.AI |
| Published | 23 July 2026 |
| Last updated at source | 30 July 2026 |
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