Adaptive auditing of AI systems with anytime-valid guarantees

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Hi, I'm Tom.

Jane: And I'm Jane, here with Lu, Meng and our in-house large language model, Lalam.

Tom: We're looking at the paper: "Adaptive auditing of AI systems with anytime-valid guarantees".

Jane: Here's the gist of the paper. The gist The authors introduce a hypothesis testing framework leveraging Safe Anytime-Valid Inference (SAVI) to rigorously characterize failure modes in generative AI systems by formalizing auditing as dueling hypotheses between the model's null and the auditor's null, which allows for statistically rigorous conclusions with finite-sample guarantees.

Tom: Formalization of Auditing Hypotheses The paper formalizes AI robustness auditing as conducting two “dueling” hypothesis tests: one from the perspective of the “model” and one from the perspective of the “auditor” (Figure 1(a)) The auditor’s null hypothesis asserts that they have a strategy that will eventually identify a failure mode Under an asymptotically consistent auditing strategy π, the model’s and auditor’s null hypotheses are asymptotically complementary, i.e., lim m→∞ Haud,

m: 1 true ⇐⇒ Hmod0 true and lim m→∞ Haud,

m: 0 true ⇐⇒ Hmod1 true These bets are translated into e-processes—the SAVI analog of the p-value in the sequential setting—which provide valid finite-sample Type-I error control under arbitrary adaptive sampling and optional stopping The key property of e-processes is that they are guaranteed to control the Type I error rate under adaptive sampling and optional stopping by Ville’s inequality Testing Procedures for Model Null To test the model’s null hypothesis, several e-process formulations are considered Options 3 and 4 involve integrating changepoint detection techniques into aforementioned e-processes, such as SR-LR and SR-LR-UI Testing Procedures for Auditor Null It is proven that there is no anytime-valid testing procedure that can achieve higher power than its Type I error prior to the m-th observation The dual testing procedure combines these e-processes for both nulls, and it is shown that this dual testing procedure with stopping thresholds 1/α provides safe any-time valid control of the FWER at level α Empirical Results Empirically, the proposed testing procedures maintain anytime-valid type-I error control, outperform pre-specified testing methods, and can reach statistically rigorous conclusions sometimes with as few as 20 observations Experiment 1 demonstrates that SR-LR-UI achieved the highest power for detecting failure modes (Fig 2) Furthermore, in Experiment 2, the adaptive auditor indeed learns to concentrate on the low-accuracy categories (Figure 3 right) This work provides a general statistical framework for common auditing practices of modern AI systems Future directions include optimizing the auditing strategy as this work aims to provide a statistical foundation for arbitrarily adaptive auditing strategies and treats auditing strategies as a black box It is hoped that by providing a general statistical framework for common auditing practices of modern AI systems, we can empower practitioners to confidently assess AI robustness without sacrificing statistical validity Page 1 --- The gist The authors introduce a hypothesis testing framework leveraging Safe Anytime-Valid Inference (SAVI) to rigorously characterize failure modes in generative AI systems by formalizing auditing as dueling hypotheses between the model's null and the auditor's null, which allows for statistically rigorous conclusions with finite-sample guarantees.

Jane: Formalization of Auditing Hypotheses The paper formalizes AI robustness auditing as conducting two “dueling” hypothesis tests: one from the perspective of the “model” and one from the perspective of the “auditor” (Figure 1(a)) The auditor’s null hypothesis asserts that they have a strategy that will eventually identify a failure mode Under an asymptotically consistent auditing strategy π, the model’s and auditor’s null hypotheses are asymptotically complementary, i.e.

Paper summary: Tom: So we've covered the basics of "Adaptive auditing of AI systems with anytime-valid guarantees," looking at how they formalize auditing as dueling hypotheses between the model and the auditor, and how they use e-processes to ensure valid inferences under adaptive testing.

Jane: It really comes back to addressing that bottleneck where annotation is too costly, by showing that you can still draw statistically rigorous conclusions even with very limited data sets.

Lu: The paper provides a framework for arbitrary sampling and stopping schemes, which is a big thing because it overcomes the classical constraints of active testing settings that require a pre-specified sampling scheme

Berrada et al., two thousand twenty-five Kuang et al <ref:2605.07002#pg2>., two thousand twenty-five: <ref:2605.07002#pg2>.

Meng: I’m still thinking about what it costs to actually run these adaptive procedures in production when you're monitoring a model continuously.

Lalam: The authors are providing a general statistical framework for common auditing practices of modern AI systems, which should empower practitioners to confidently assess AI robustness without sacrificing statistical validity.

Conclusion: Tom: So, this paper is about adaptive auditing for AI systems, and the title is "Adaptive auditing of AI systems with anytime-valid guarantees."

Jane: It’s pretty simple—it tackles how we test if an AI is actually safe when we don't know exactly what to test beforehand.

Lu: The core idea they introduce is using a hypothesis testing framework based on something called Safe Anytime-Valid Inference, or SAVI.

Meng: So, instead of setting a fixed plan for testing the AI, this method lets you decide how much you test based on what you've already seen during the process.

Lalam: Basically they turn auditing into a bet between two hypotheses: one where the model is actually failing, and one where your testing strategy will eventually find that failure.

Tom: And they formalize this by showing these two nulls are asymptotically complementary, which means if you test enough times, finding a failure mode on the first null implies the other thing is true.

Jane: That sounds really powerful because it moves past just saying "this method works" to actually giving you a statistical guarantee about the stopping rule.

Lu: The paper shows that under this specific setup, they can control Type-I errors—which means false alarms—even when you are picking cases one by one adaptively.

Meng: That’s good because in production, I don't want a test that gives me a false alarm every time I run it on live traffic.

Lalam: They actually show empirical results where this approach can reach statistically rigorous conclusions with as few as twenty observations, which is way less than what we usually need.

Tom: So the implication here is that we might be able to do more thorough safety checks on complex AI systems without needing massive amounts of data upfront.

Jane: It really shifts the focus from designing a perfect testing strategy to building a statistical framework that handles the uncertainty of real-world, adaptive testing.