Investigating Data Interventions for Subgroup Fairness: An ICU Case Study

Algorithmic fairness methods are increasingly used to identify and mitigate bias in machine learning models, yet most approaches are evaluated in isolation and along single demographic axes. This limits practical guidance for selecting fairness strategies, where disparities may arise across intersectional subgroups and across multiple stages of the modeling lifecycle. This work presents FairSelect, a toolkit for systematically evaluating fairness mitigation strategies applied individually and in combination across preprocessing, inprocessing, and postprocessing stages. FairSelect supports multiple model architectures, intersectional subgroup evaluation, and comparison of fairness utility tradeoffs across baseline, single method, and multi level configurations. The framework was validated using synthetic clinical datasets designed to represent specific bias mechanisms and a real-world replication of two-year stroke risk prediction among patients with atrial fibrillation. Synthetic experiments showed that targeted fairness methods generally reduced intended subgroup disparities, while combined strategies produced larger average fairness improvements with modest utility tradeoffs. In the clinical prediction task, mitigation effects were highly variable, with some combinations improving both fairness and predictive performance while others were ineffective or counterproductive. These findings demonstrate that fairness interventions interact in nonadditive and context dependent ways. FairSelect provides a practical framework for systematically identifying fairness strategies that improve subgroup equity while preserving model performance in clinical machine learning.

Medical image segmentation models can perform unevenly across subgroups. Most existing fairness methods focus on improving average subgroup performance, implicitly treating each subgroup as internally homogeneous. However, this can hide difficult cases within a subgroup, where high-loss samples are obscured by the subgroup mean. We call this problem intra-group hidden failure. To solve this, we propose DuetFair mechanism, a dual-axis fairness framework that jointly considers inter-subgroup adaptation and intra-subgroup robustness. Based on DuetFair, we introduce FairDRO, which combines distribution-aware mixture-of-experts (dMoE) with subgroup-conditioned distributionally robust optimization (DRO) loss aggregation. This design allows the model to adapt across subgroups while also reducing hidden failures within each subgroup. We evaluate FairDRO on three medical image segmentation benchmarks with varying degrees of within-group heterogeneity. FairDRO achieves the best equity-scaled performance on Harvard-FairSeg and improves worst-case subgroup performance on HAM10000 under both age- and race-based grouping schemes. On the 3D radiotherapy target cohort, FairDRO further improves worst-group Dice by 3.5 points ($\uparrow 6.0\%$) under the tumor-stage grouping and by 4.1 points ($\uparrow 7.4\%$) under the institution grouping over the strongest baseline.

Machine learning (ML) models used in clinical workflows often perform systematically differently across patient groups, raising concerns about equity and patient safety. Traditional subgroup analyses can expose such disparities only when rich metadata (e.g., patient demographics) is available and captures the sources of performance variation, conditions rarely met in practice. Algorithmic subgroup discovery methods offer a solution by identifying coherent groups from learned feature representations, but because true underlying stratifications are unknown, these methods are hard to evaluate. As a result, subgroup discovery has not been systematically applied or assessed for performance monitoring. Our main objective is to evaluate algorithmic subgroup discovery for performance monitoring, assessing the discovered subgroups' performance gaps and semantic coherence. Investigating cardiomegaly classification in a dataset of 223,228 chest X-rays, we showed that the discovered subgroups exhibited larger performance gaps (accuracies of 0.61-0.90) than traditional metadata-based analysis (0.80-0.94) and were frequently interpretable using patient, acquisition, and model metadata. As a proof of concept, we also showed that annotations from learned subgroups can be used to mitigate the biases they expose. We conclude that subgroup discovery is a practical and effective tool to enhance the performance validation and monitoring of ML systems in healthcare.