Superhuman FairnessDownload PDF

Published: 04 Mar 2023, Last Modified: 28 Mar 2023ICLR 2023 Workshop on Trustworthy ML PosterReaders: Everyone
Keywords: Fairness, Classification, Imitation Learning
TL;DR: This paper re-casts fair machine learning as an imitation learning task by introducing superhuman fairness, which seeks to simultaneously outperform human decisions on multiple predictive performance and fairness measures.
Abstract: The fairness of machine learning-based decisions has become an increasingly important focus in the design of supervised machine learning methods. Most fairness approaches optimize a specified trade-off between performance measure(s) (e.g., accuracy, log loss, or AUC) and fairness metric(s) (e.g., demographic parity, equalized odds). This begs the question: are the right performance-fairness trade-offs being specified? We instead re-cast fair machine learning as an imitation learning task by introducing superhuman fairness, which seeks to simultaneously outperform human decisions on multiple predictive performance and fairness measures. We demonstrate the benefits of this approach given suboptimal decisions.
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