Pairwise Fairness for Ordinal Regression

Matthäus Kleindessner, Samira Samadi, Muhammad Bilal Zafar, Krishnaram Kenthapadi, Chris Russell
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, PMLR 151:3381-3417, 2022.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v151-kleindessner22a, title = { Pairwise Fairness for Ordinal Regression }, author = {Kleindessner, Matth\"aus and Samadi, Samira and Bilal Zafar, Muhammad and Kenthapadi, Krishnaram and Russell, Chris}, booktitle = {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics}, pages = {3381--3417}, year = {2022}, editor = {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel}, volume = {151}, series = {Proceedings of Machine Learning Research}, month = {28--30 Mar}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v151/kleindessner22a/kleindessner22a.pdf}, url = {https://proceedings.mlr.press/v151/kleindessner22a.html}, 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. } }
Endnote
%0 Conference Paper %T Pairwise Fairness for Ordinal Regression %A Matthäus Kleindessner %A Samira Samadi %A Muhammad Bilal Zafar %A Krishnaram Kenthapadi %A Chris Russell %B Proceedings of The 25th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2022 %E Gustau Camps-Valls %E Francisco J. R. Ruiz %E Isabel Valera %F pmlr-v151-kleindessner22a %I PMLR %P 3381--3417 %U https://proceedings.mlr.press/v151/kleindessner22a.html %V 151 %X 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.
APA
Kleindessner, M., Samadi, S., Bilal Zafar, M., Kenthapadi, K. & Russell, C.. (2022). Pairwise Fairness for Ordinal Regression . Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 151:3381-3417 Available from https://proceedings.mlr.press/v151/kleindessner22a.html.

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