KAISEN: Reproducible Subgroup Fairness Auditing for Clinical Risk Models
Score
73首次系统揭示审计流水线各组件的失效边界，并给出量化证据，填补了公平性审计方法论的可信度空白。
方法设计严谨，基于合成基准提供真实标签，统计指标完整，结论清晰且可复现。
为审计实践提供明确警示：应报告方差而非均值，阈值不可跨队列迁移，可直接指导临床模型评估。
发布在arXiv，属于预印本，但主题紧贴当前AI公平性与临床部署监管热点。
来自arXiv顶级分类AI/CL/LG，作者提供完整代码与数据，可信度高。
Base 81.5 × T2 Source weight ×0.9 = 73
Curation line 70Selected
Why selected
This paper innovatively treats the audit pipeline itself as the object of study, systematically exposing component failure modes via synthetic benchmarks, offering rare transparency and practical guidance.
AI commentary
「What makes this paper worth reading is that it puts the audit tools themselves under the microscope—finding that seemingly reasonable audit components can fail silently at critical moments. For any team responsibly evaluating clinical AI, this belongs on the mandatory reading list. Its emphasis on reporting variance rather than averages is a much-needed wake-up call for current fairness reporting practice.」
Quick overview
KAISEN is a reproducible subgroup fairness auditing pipeline for clinical risk models, covering subgroup stratification, disparity measurement, mechanism diagnostics, post-hoc mitigation, and drift monitoring. Using a synthetic benchmark of 16 disease tasks, 15 social-determinant axes from Healthy People 2030, and three prespecified intersections, it finds: significance counts track raw equalized-odds difference only after standardization by minimum detectable effect; per-group threshold optimization reduces EOD in all 48 held-out runs, while Platt scaling behaves like a coin flip; the mechanism diagnostic fails silently under proxy misspecification; and CUSUM drift detection thresholds do not transfer across cohorts. The audit should report variance, not averages.
- KAISEN is a five-phase audit pipeline stress-tested to failure on a synthetic benchmark;
- Per-group threshold optimization reduces EOD in 48/48 held-out runs, while Platt scaling improves only 19/48;
- The mechanism diagnostic classifies 144/144 controlled cases but recovers zero model-driven cases under proxy misspecification, with no failure signal;
- CUSUM drift detection at a reference threshold yields 27 false alarms and 7/8 missed shifts across cohorts, showing thresholds do not transfer.
Full text
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