We initiate the study of fairness for ordinal regression. 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 has the form 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 provide generalization guarantees on the error and fairness violation of our predictor, and we illustrate the effectiveness of our approach in extensive experiments.
@article{arxiv.2105.03153,
title = {Pairwise Fairness for Ordinal Regression},
author = {Matthäus Kleindessner and Samira Samadi and Muhammad Bilal Zafar and Krishnaram Kenthapadi and Chris Russell},
journal= {arXiv preprint arXiv:2105.03153},
year = {2022}
}