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arXiv Machine Learning By Isaac Gibbs, Elise L. Han, Ryan J. Tibshirani, Tijana Zrnic

Reliable AI-Assisted Decisions via Selective Auditing

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The paper introduces a framework for online monitoring of AI systems that produce decisions with associated confidence scores. It formulates the problem as an online constrained optimization task, aiming to minimize expert auditing while ensuring that the proportion of unaudited AI errors remains below a user‑defined threshold. The authors present a family of algorithms with provable error‑control guarantees, including an optimally efficient method that achieves the best possible auditing rate for both adversarial and i.i.d. data streams, and demonstrate its effectiveness on tasks such as screening harmful requests, text annotation, and data labeling.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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