A Simplified Metric to Streamline Between-Group Fairness Assessment for Predictive Models: Algorithm Development and Evaluation Study.

Authors

Haoyuan Wang, Chuan Hong, Michael Pencina, Matthew Engelhard

Published in

JMIR AI. Volume 5. Pages e85822. Aug 24, 2026. Epub Aug 24, 2026.

Abstract

Fairness evaluation is essential for trustworthy clinical risk prediction. However, existing fairness-oriented discrimination metrics either ignore cross-group comparisons or rely on exhaustive pairwise evaluations, making them difficult to interpret and impractical for model selection.
This study aimed to develop and evaluate novel fairness-oriented discrimination metrics for clinical risk prediction that address limitations of within-group and pairwise cross-group approaches.
We examined theoretical properties of existing U-statistic-based metrics, including concordance index (CI) and area under the receiver operating characteristic curve (AUC), when applied to subgroups. We highlighted the distinction between within-group discrimination (ranking within a subgroup) and group-level discrimination (ranking relative to the broader population). Building on this framework, we proposed group-level extensions of the CI and AUC that summarize subgroup-specific performance in a single interpretable measure. We then applied these metrics to the PREVENT (Predicting Risk of Cardiovascular Disease Events) equation, a recently developed model for atherosclerotic cardiovascular disease.
The traditional subgroup-specific CI and AUC captured within-group but not group-level discrimination, obscuring inequities in clinical decision-making. Existing cross-group approaches (eg, the xCI and xAUC metrics) addressed this limitation but became computationally and interpretively burdensome with multiple subgroups due to pairwise comparisons. Our proposed metrics provided a streamlined alternative, yielding 1 summary statistic per subgroup while retaining sensitivity to cross-group ranking disparities. Applied to PREVENT, these metrics revealed differences in subgroup performance not apparent from within-group evaluations.
By distinguishing between within-group and group-level discrimination, our framework clarifies a common source of misinterpretation in fairness evaluation. The proposed group-level extensions of the CI and AUC provide practical, interpretable tools for evaluating fairness in clinical prediction models, enabling more transparent and equitable risk assessment.

PMID:
42636434
Bibliographic data and abstract were imported from PubMed on 25 Aug 2026.

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