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• W3198438967 abstract "Methods for building fair predictors often involve tradeoffs between fairness and accuracy and between different fairness criteria. Recent work seeks to characterize these tradeoffs in specific problem settings, but these methods often do not accommodate users who wish to improve the fairness of an existing benchmark model without sacrificing accuracy, or vice versa. These results are also typically restricted to observable accuracy and fairness criteria. We develop a flexible framework for fair ensemble learning that allows users to efficiently explore the fairness-accuracy space or to improve the fairness or accuracy of a benchmark model. Our framework can simultaneously target multiple observable or counterfactual fairness criteria, and it enables users to combine a large number of previously trained and newly trained predictors. We provide theoretical guarantees that our estimators converge at fast rates. We apply our method on both simulated and real data, with respect to both observable and counterfactual accuracy and fairness criteria. We show that, surprisingly, multiple unfairness measures can sometimes be minimized simultaneously with little impact on accuracy, relative to unconstrained predictors or existing benchmark models." @default.
• W3198438967 created "2021-09-13" @default.
• W3198438967 creator A5000142504 @default.
• W3198438967 creator A5052315912 @default.
• W3198438967 date "2022-06-20" @default.
• W3198438967 modified "2023-09-27" @default.
• W3198438967 title "FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomes" @default.
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• W3198438967 doi "https://doi.org/10.1145/3531146.3533167" @default.
• W3198438967 hasPublicationYear "2022" @default.
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