Adaptive auditing of AI systems with anytime-valid guarantees

2026-05-11 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Research Methodology & Innovation · Depth: Expert, extended

Summary

This work introduces a novel hypothesis testing framework for adaptively auditing generative AI systems, addressing the challenge of drawing statistically rigorous conclusions from small, adaptively selected test suites (typically 10-50 cases). The framework formalizes AI robustness auditing through "dueling" null hypotheses: the model's null ($H_{0}^{\texttt{mod}}$) asserts no failure modes exist below a target threshold, while the auditor's null ($H_{0}^{\texttt{aud},m}$) asserts a sampling strategy will uncover a failure mode within a budget $m$. Leveraging Safe Anytime-Valid Inference (SAVI) and "testing by betting," the authors develop e-process-based procedures (Likelihood Ratios, LR-UI, SR-LR, SR-LR-UI) that maintain anytime-valid Type-I error control under arbitrary adaptive sampling and optional stopping. Empirical results on semi-synthetic data and a real-world LLM pipeline for clinical note analysis demonstrate that these adaptive testing methods, particularly SR-LR-UI, outperform pre-specified methods, achieving statistically rigorous conclusions with as few as 20 observations while controlling Type I error.

Key takeaway

For NLP Engineers or Research Scientists developing or deploying generative AI, understanding this adaptive auditing framework is crucial. It allows you to conduct statistically sound evaluations of AI robustness and identify failure modes efficiently, even with limited annotation budgets. You can confidently assess system reliability and certify robustness by employing e-process-based procedures like SR-LR-UI, which significantly outperform traditional pre-specified testing methods and provide strong statistical guarantees.

Key insights

Adaptive AI auditing can achieve statistical rigor using dueling hypotheses and anytime-valid e-processes, even with small, flexible datasets.

Principles

• Adaptive testing violates classical statistical assumptions.
• Dueling null hypotheses clarify what can be tested in adaptive audits.
• E-processes provide finite-sample Type-I error control.

Method

Formalize AI auditing with dueling null hypotheses ($H_{0}^{\texttt{mod}}$ and $H_{0}^{\texttt{aud},m}$), then apply SAVI-based e-processes (LR, LR-UI, SR-LR, SR-LR-UI) for anytime-valid Type-I error control under adaptive sampling and optional stopping.

In practice

• Use SR-LR-UI for highest power in adaptive failure mode detection.
• Adaptive auditors can concentrate on low-accuracy categories.
• Rigorous conclusions are possible with 20-50 observations.

Topics

• Adaptive Auditing
• AI Robustness
• Safe Anytime-Valid Inference
• e-processes
• Dueling Hypothesis Tests

Best for: NLP Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.