KAISEN把公平性审计拆成五个阶段，用合成数据测出哪个环节会失效。阈值优化靠谱，但Platt缩放和机制诊断有坑。
KAISEN是一种针对临床风险模型的五阶段公平性审计流程，在包含16个疾病任务和15个健康社会决定因素轴的合成基准上测试。研究发现，显著性计数与原始均等化赔率差（EOD）的秩相关为0.56，按最小可检测效应标准化后升至0.78。每组的阈值优化在48次运行中全部降低EOD（配对差值-0.285），而组级Platt缩放的EOD改善仅19/48次。机制诊断在144个受控案例中全部正确，但在48个模型驱动案例的代理设定错误下全部失败。CUSUM警报和漏报主要受队列随机种子影响（卡方p=0.002），阈值无法跨队列迁移。
KAISEN: Reproducible Subgroup Fairness Auditing for Clinical Risk Models
Clinical risk models routinely achieve strong aggregate performance while producing materially different error rates across patient subgroups. Audit pipelines have been proposed to catch this, but their components are rarely stress-tested, so it is unclear which parts of an audit can be trusted and under what conditions. We present KAISEN, a five-phase audit pipeline covering subgroup stratification, disparity measurement, mechanism diagnostics, post-hoc mitigation, and drift monitoring, evaluated to the point of failure on a synthetic benchmark of 16 disease tasks, 15 social-determinant axes from Healthy People 2030, and three prespecified intersections. Four findings follow. (i) Significance tracks each axis's gap against its own minimum detectable effect: rank correlation between significance count and raw equalized-odds difference (EOD) across the 15 axes is rho = 0.56, rising to rho = 0.78 once EOD is standardized by that floor. (ii) Per-group threshold optimization reduces EOD in 48 of 48 held-out runs (paired delta = -0.285, 95% CI [-0.313, -0.252]), while group-wise Platt scaling -- the better calibrator -- behaves as a coin flip on EOD (19 of 48 runs improved, 95% CI [0.26, 0.55]) with mean effect near zero, so what an audit should report is the variance, not the average. (iii) The mechanism diagnostic classifies 144 of 144 controlled cases correctly but recovers none of 48 model-driven cases under proxy misspecification, with no signal that it failed. (iv) CUSUM failures and false alarms track cohort realization far more than disease: at the reference threshold, all 27 false alarms and 7 of 8 missed shifts come from different seeds (chi-squared p = 0.002), so a threshold tuned on one cohort fails to transfer. All results are synthetic with known ground truth and do not establish clinical validity. Code, artifacts, and scripts reproducing every number are released.