Poster

Pairwise Fairness for Ordinal Regression

Matthäus Kleindessner · Samira Samadi · Muhammad Bilal Zafar · Krishnaram Kenthapadi · Chris Russell

[ Abstract ]
Tue 29 Mar 1 a.m. PDT — 2:30 a.m. PDT

Abstract:

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.

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