Pairwise Fairness for Ordinal Regression

• URL: http://arxiv.org/abs/2105.03153v1
• Date: Fri, 7 May 2021 10:33:42 GMT
• Title: Pairwise Fairness for Ordinal Regression
• Authors: Matth\"aus Kleindessner, Samira Samadi, Muhammad Bilal Zafar, Krishnaram Kenthapadi, Chris Russell
• Abstract summary: We adapt two fairness notions previously considered in fair ranking and propose a strategy for training a predictor that is approximately fair according to either notion. Our predictor consists of a threshold model, composed of a scoring function and a set of thresholds. We show that our strategy allows us to effectively explore the accuracy-vs-fairness trade-off and that it often compares favorably to "unfair" state-of-the-art methods for ordinal regression.
• Score: 22.838858781036574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We initiate the study of fairness for ordinal regression, or ordinal classification. We adapt two fairness notions previously considered in fair ranking and propose a strategy for training a predictor that is approximately fair according to either notion. Our predictor consists of a threshold model, composed of a scoring function and a set of thresholds, and our strategy is based on a reduction to fair binary classification for learning the scoring function and local search for choosing the thresholds. We can control the extent to which we care about the accuracy vs the fairness of the predictor via a parameter. In extensive experiments we show that our strategy allows us to effectively explore the accuracy-vs-fairness trade-off and that it often compares favorably to "unfair" state-of-the-art methods for ordinal regression in that it yields predictors that are only slightly less accurate, but significantly more fair.

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