Related papers: Superhuman Fairness

Superhuman Fairness

URL: http://arxiv.org/abs/2301.13420v1
Date: Tue, 31 Jan 2023 05:23:53 GMT
Title: Superhuman Fairness
Authors: Omid Memarrast, Linh Vu, Brian Ziebart
Abstract summary: We re-cast fair machine learning as an imitation learning task by introducing superhuman fairness. We demonstrate the benefits of this approach given suboptimal decisions.
Score: 2.610470075814367
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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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