Fairness and Bias in Online Selection

Jose Correa, Andres Cristi, Paul Duetting, Ashkan Norouzi-Fard
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:2112-2121, 2021.

Abstract

There is growing awareness and concern about fairness in machine learning and algorithm design. This is particularly true in online selection problems where decisions are often biased, for example, when assessing credit risks or hiring staff. We address the issues of fairness and bias in online selection by introducing multi-color versions of the classic secretary and prophet problem. Interestingly, existing algorithms for these problems are either very unfair or very inefficient, so we develop optimal fair algorithms for these new problems and provide tight bounds on their competitiveness. We validate our theoretical findings on real-world data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-correa21a, title = {Fairness and Bias in Online Selection}, author = {Correa, Jose and Cristi, Andres and Duetting, Paul and Norouzi-Fard, Ashkan}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {2112--2121}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/correa21a/correa21a.pdf}, url = {https://proceedings.mlr.press/v139/correa21a.html}, abstract = {There is growing awareness and concern about fairness in machine learning and algorithm design. This is particularly true in online selection problems where decisions are often biased, for example, when assessing credit risks or hiring staff. We address the issues of fairness and bias in online selection by introducing multi-color versions of the classic secretary and prophet problem. Interestingly, existing algorithms for these problems are either very unfair or very inefficient, so we develop optimal fair algorithms for these new problems and provide tight bounds on their competitiveness. We validate our theoretical findings on real-world data.} }
Endnote
%0 Conference Paper %T Fairness and Bias in Online Selection %A Jose Correa %A Andres Cristi %A Paul Duetting %A Ashkan Norouzi-Fard %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-correa21a %I PMLR %P 2112--2121 %U https://proceedings.mlr.press/v139/correa21a.html %V 139 %X There is growing awareness and concern about fairness in machine learning and algorithm design. This is particularly true in online selection problems where decisions are often biased, for example, when assessing credit risks or hiring staff. We address the issues of fairness and bias in online selection by introducing multi-color versions of the classic secretary and prophet problem. Interestingly, existing algorithms for these problems are either very unfair or very inefficient, so we develop optimal fair algorithms for these new problems and provide tight bounds on their competitiveness. We validate our theoretical findings on real-world data.
APA
Correa, J., Cristi, A., Duetting, P. & Norouzi-Fard, A.. (2021). Fairness and Bias in Online Selection. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:2112-2121 Available from https://proceedings.mlr.press/v139/correa21a.html.

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