Fair Logistic Regression: An Adversarial Perspective release_6moo2j4fjfbozd4saj3di5tpyi

by Ashkan Rezaei, Rizal Fathony, Omid Memarrast, Brian Ziebart

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2019  

Abstract

Fair prediction methods have primarily been built around existing classification techniques using pre-processing methods, post-hoc adjustments, reduction-based constructions, or deep learning procedures. We investigate a new approach to fair data-driven decision making by designing predictors with fairness requirements integrated into their core formulations. We augment a game-theoretic construction of the logistic regression model with fairness constraints, producing a novel prediction model that robustly and fairly minimizes the logarithmic loss. We demonstrate the advantages of our approach on a range of benchmark datasets for fairness.
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Date   2019-03-19
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arXiv  1903.03910v2
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