Homogeneous deep ensembles shrink accuracy gaps between demographic groups without lowering overall accuracy, and the optimal training-data balance shifts toward the harder group when per-group task difficulty differs.
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Fairness of Deep Ensembles: On the interplay between per-group task difficulty and under-representation
Homogeneous deep ensembles shrink accuracy gaps between demographic groups without lowering overall accuracy, and the optimal training-data balance shifts toward the harder group when per-group task difficulty differs.