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.
| Author(s): | Matthäus Kleindessner and Samira Samadi and Muhammad Bilal Zafar and Krishnaram Kenthapadi and Chris Russell |
| Links: | |
| Journal: | arXiv preprint arXiv:2105.03153 |
| Year: | 2021 |
| Bibtex Type: | Article (article) |
| Electronic Archiving: | grant_archive |
BibTex
@article{fair-ordinal-regression,
title = {Pairwise Fairness for Ordinal Regression},
journal = {arXiv preprint arXiv:2105.03153},
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.},
year = {2021},
slug = {fair-ordinal-regression},
author = {Kleindessner, Matth{\"a}us and Samadi, Samira and Zafar, Muhammad Bilal and Kenthapadi, Krishnaram and Russell, Chris}
}