Derives closed-form optimal counterfactually fair regressor via barycentric quantile map and proves Õ(n^{-1/3}) finite-sample fairness and risk bounds for discretized post-processing under mild assumptions.
Survey on causal-based machine learning fairness notions, 2022
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A literature survey of 164 papers on software fairness reveals gaps in requirements engineering, intersectional measures, unstructured data, and white-box ML methods.
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Counterfactually Fair Regression via Optimal Transport
Derives closed-form optimal counterfactually fair regressor via barycentric quantile map and proves Õ(n^{-1/3}) finite-sample fairness and risk bounds for discretized post-processing under mild assumptions.
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Software Fairness: An Analysis and Survey
A literature survey of 164 papers on software fairness reveals gaps in requirements engineering, intersectional measures, unstructured data, and white-box ML methods.