KAISEN: Reproducible Subgroup Fairness Auditing for Clinical Risk Models
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TL;DR - KAISEN is a reproducible, five-phase framework for auditing subgroup fairness in clinical risk models. Synthetic stress tests show that mitigation, diagnostics, and drift-monitoring components can fail unpredictably or silently.
- Per-group threshold optimization reduced equalized-odds disparity in all 48 held-out runs.
- Group-wise Platt scaling improved calibration but had inconsistent, near-zero average effects on fairness.
- Mechanism diagnostics identified all controlled cases but missed every model-driven case under proxy misspecification without warning.
- CUSUM drift thresholds transferred poorly across cohort realizations; the synthetic results do not establish clinical validity.
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KAISEN: Reproducible Subgroup Fairness Auditing for Clinical Risk Models
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TL;DR - KAISEN is a reproducible, five-phase framework for auditing subgroup fairness in clinical risk models. Synthetic stress tests show that mitigation, diagnostics, and drift-monitoring components can fail unpredictably or silently.
- Per-group threshold optimization reduced equalized-odds disparity in all 48 held-out runs.
- Group-wise Platt scaling improved calibration but had inconsistent, near-zero average effects on fairness.
- Mechanism diagnostics identified all controlled cases but missed every model-driven case under proxy misspecification without warning.
- CUSUM drift thresholds transferred poorly across cohort realizations; the synthetic results do not establish clinical validity.