Fair Ensembling

Use ensembling for fairness. Learn diversity-promoting ensembles (different feature subsets, architectures, data slices), group-specialist mixtures with gating, fairness-aware bagging, ensemble calibration per group, and the compute-cost-vs-fairness profile that decides whether ensembling is the right tool.