Original title: Exact Finite-Population Sequential Auditing for Selective Release from Model-Proposed Candidate Sets
Yusheng Hu, Zhiyuan Zhou, Yongfeng Zhu, Kewei Liang · September 2026
Computer models can flag cases that seem safe to skip, but past success does not guarantee today's batch is safe to pass without checks. Researchers created a step-by-step auditing method to solve this. First, they freeze a list of candidate cases and a testing plan before anyone checks the outcomes. Next, they randomly pick and inspect cases one at a time without putting them back. Depending on what they find, the plan chooses to release the batch, keep checking, or inspect every single case. Tested across 675 generic setups, the chance of an unsafe release stayed below 3.998% under a 5% limit. This process helps teams safely skip reviews while reliably catching defects.
A factory has a batch of products to ship. It cannot assume the current batch has zero defects just because past batches were good. So the quality team randomly inspects a few items: if too many defects are found, they inspect everything; otherwise, they ship the rest. In this paper, a predictive model flags which cases are likely safe, and the audit uses random sampling to decide release, ensuring the chance of releasing a defective case stays below a chosen limit.
Industries like fraud detection and medical screening rely on models to flag low-risk cases for fast processing. But past accuracy does not guarantee safety for a new batch. This method provides a mathematical guarantee on the risk of missing a defect, allowing safe skipping of many reviews and saving significant time and resources.
The abstract states these are operating characteristics under audit randomization, not observed factory savings. Candidate construction and conservative adjustment were selected during development, which may limit generalizability. The method has not been tested on other datasets or real-world deployment.
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Exact Finite-Population Sequential Auditing for Selective Release from Model-Proposed Candidate Sets — Yusheng Hu, Zhiyuan Zhou, Yongfeng Zhu et al. — Mathematics — doi:10.3390/math14183427
Abstract licence: CC BY
Explanation generated by Synthesis from the abstract — it is not the original paper.