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Methods for building fair predictors often involve tradeoffs between fairnessand accuracy and between different fairness criteria, but the nature of thesetradeoffs varies. Recent work seeks to characterize these tradeoffs in specificproblem settings, but these methods often do not accommodate users who wish toimprove the fairness of an existing benchmark model without sacrificingaccuracy, or vice versa. These results are also typically restricted toobservable accuracy and fairness criteria. We develop a flexible framework forfair ensemble learning that allows users to efficiently explore thefairness-accuracy space or to improve the fairness or accuracy of a benchmarkmodel. Our framework can simultaneously target multiple observable orcounterfactual fairness criteria, and it enables users to combine a largenumber of previously trained and newly trained predictors. We providetheoretical guarantees that our estimators converge at fast rates. We apply ourmethod on both simulated and real data, with respect to both observable andcounterfactual accuracy and fairness criteria. We show that, surprisingly,multiple unfairness measures can sometimes be minimized simultaneously withlittle impact on accuracy, relative to unconstrained predictors or existingbenchmark models.

FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomes