Fairness Metrics based on the AUC in Clinical Predictive AI

Paper recorded by Signals 4 on 2026-11-03 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-11-03 on arXiv · recorded by Signals 4 on 2026-11-04

Category: cs.AI · 人工智能 · first seen 2026-11-04

Abstract

Fairness evaluation of clinical prediction models for binary outcomes often involves fairness metrics that compare the area under the receiver operating characteristic curve (AUC, or c-statistic) across subgroups. However, the AUC has been shown to be a non-collapsible metric, meaning that its subgroup specific and overall values are not directly comparable as often implicitly assumed. This raises

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