safety security
The Auditor Does Not Need to Read Everything
A selective-review policy minimizes expert checks while controlling how many AI errors escape unaudited, without assuming a particular data distribution.
Summary
A selective-review policy minimizes expert checks while controlling how many AI errors escape unaudited, without assuming a particular data distribution.
AI-assisted decisions often arrive with confidence scores, but reviewing every proposal defeats much of the automation. Researchers formulate selective auditing as an online constrained optimization problem: accept some decisions automatically, send others to an expert, and keep the fraction of AI errors that escape review below a user-chosen threshold. Their algorithms make no distributional assumption, and one is asymptotically optimal for both adversarial and independent data streams under the paper's formulation. Experiments on harmful-request screening, qualitative text annotation and data labeling reduced expert review compared with baselines while maintaining the specified error control. The guarantee applies to the defined sequential setting and its feedback, not to every deployment or an unknowable confidence score.
Why it matters
A selective-review policy minimizes expert checks while controlling how many AI errors escape unaudited, without assuming a particular data distribution.
Limits and context
- The guarantee applies to the defined sequential setting and its feedback, not to every deployment or an unknowable confidence score.
Key claims
A selective-review policy minimizes expert checks while controlling how many AI errors escape unaudited, without assuming a particular data distribution.
Qualification: The guarantee applies to the defined sequential setting and its feedback, not to every deployment or an unknowable confidence score.
Evidence: source-2026-10-23-002
Sources
- arXiv preprint 2610.31662, original v1arXiv · primary research
Corrections
No corrections have been recorded for this story.