Selective auditing reduces human reviews but keeps AI mistakes in check
Problem: AI makes decisions; who checks them? The study tests a policy to audit fewer items while keeping unreviewed errors near a target.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the paper
- Authors
- Isaac Gibbs, Elise L. Han, Ryan J. Tibshirani, Tijana Zrnic
- Universities and research institutions
- Not yet supplied in verified metadata; the Brief does not guess.
What they did and found
The paper studies online selective auditing, a rule to decide when a human should review results. It keeps errors left unchecked near a target while reducing reviews.
Why it matters
For organizations, safer AI use can happen with less time spent on constant checks. It helps direct auditor work to the riskiest outputs.
The authors suggest a simple rule: flag results for human review when unsure, but still test some confident cases to learn and adjust.
In practice, a finance team could audit only a fraction of automated checks and gradually improve the system’s confidence judgments to save time while staying reliable.
What remains uncertain
Results depend on testing conditions, cost of audits, and data patterns; real-world outcomes may differ in practice across contexts and times.
Original sources · 1
- Reliable AI-Assisted Decisions via Selective Auditing ↗arXiv · 2026-10-22
Check the original paper for its authors, methods, version and access terms.
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