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FairMoE-Health: Fairness-Aware Mixture of Experts for Equitable Multimodal Clinical Prediction
Journal article   Peer reviewed

FairMoE-Health: Fairness-Aware Mixture of Experts for Equitable Multimodal Clinical Prediction

Xiaoyang Wang and Christopher C Yang
IEEE Journal of Biomedical and Health Informatics
14 Sep 2026
DOI: https://doi.org/10.1109/JBHI.2026.3733584
PMID: 42743027
Featured in Collection :   Drexel's Newest Publications

Abstract

Mixture-of-Experts (MoE) models for multi modal clinical prediction route patients to specialized ex pert networks based on input modalities, but we show that this routing mechanism introduces a previously un recognized source of demographic bias: because modality availability (e.g., whether a chest X-ray exists) correlates with race, gender, and insurance status, the gating network learns routing patterns that systematically differ across demographic groups. We propose FairMoE Health, a fairness-aware MoE framework that intervenes at three architectural levels: adversarial debiasing of encoded representations reduces demographic information before it reaches the gating network; adversarial debiasing of gating weights further discourages demographic leakage into routing decisions; and equalized odds regularization provides a prediction-level safety net. We introduce the Routing Disparity (RD) metric to quantify demographic imbalance in expert assignments. On three clinical prediction tasks from MIMIC-IV (mortality, length-of-stay, readmission), FairMoE-Health consistently reduces racial Equalized Odds Difference (EOD) and RD across all three tasks, with only small AUROC decreases. The Routing Disparity metric and multi-level debiasing framework introduced here generalize to MoE systems operating on demographically heterogeneous populations, providing both an audit tool and an architectural intervention for a bias mechanism that existing fairness methods leave unaddressed.

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