The authors tackle a practical problem in AI auditing: when you're adaptively testing a model (deciding on-the-fly which cases to annotate based on what you've seen), classical statistics breaks down because you're peeking at the data and changing your plan mid-stream. They introduce a "testing by betting" framework using Safe Anytime-Valid Inference (SAVI) that lets auditors draw statistically rigorous conclusions from very small samples (sometimes just 20 observations) even when they're adaptively choosing what to test next. The framework sets up dueling hypotheses—the model claiming it has no failure modes below a threshold versus the auditor claiming they can find one—and proves these are asymptotically inverses, meaning passing a tough audit actually certifies robustness.
Main takeaways:
- Adaptive testing (choosing what to evaluate based on earlier results) is practical but violates classical statistics assumptions, making it hard to draw rigorous conclusions from small samples
- The "testing by betting" approach maintains statistical validity even when you change your sampling strategy mid-audit and work with only 10-50 test cases
- The framework formalizes two competing perspectives: the model's claim of no serious failures versus the auditor's claim they can find one
- If the auditor is sufficiently thorough, passing their audit provably certifies the model as globally robust (the two hypotheses become inverses)
- Empirically outperforms pre-specified (non-adaptive) testing and sometimes reaches valid conclusions with as few as 20 observations