arXiv AI By Anissa Alloula, Charles Jones, Zuzanna Wakefield-Skorniewska, Francesco Quinzan, Bart{\l}omiej Papie\.z

When does Subgroup Balance Matter? Predicting Performance Sensitivity from Latent Separability

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arXiv Machine Learning
Sep 21

Robust Mixture Models for Algorithmic Fairness Under Latent Heterogeneity

The paper introduces ROME, a framework that learns latent group structure while optimizing worst-group predictive performance. ROME links latent-variable modeling with distributionally robust optimization through an Expectation-Maximization approach for linear models and a neural Mixture-of-Experts for nonlinear settings. Experiments on simulations and three real-world regression datasets show that ROME improves worst-group performance while maintaining competitive overall accuracy compared to existing group-aware and group-label-free robust learning methods.

By Siqi Li, Molei Liu, Yiwei Lyu, Ziye Tian, Chuan Hong, Nan Liu
arXiv AI
Aug 25

Fairness-Aware Mixture-of-Experts via Subgroup Reweighting and Gate Regularization

The paper proposes a fairness-aware Mixture-of-Experts (MoE) framework that tackles routing-induced bias by applying subgroup reweighting to correct data imbalance and gate entropy regularization to prevent the gating network from collapsing onto subgroup attributes. This end-to-end approach keeps expert utilization balanced and interpretable, offering a clear view of how subgroups are allocated across experts. Experiments show that the method improves fairness while maintaining competitive predictive performance.

By Sunhee Hwang