Centralized Selection with Preferences in the Presence of Biases

L. Elisa Celis, Amit Kumar, Nisheeth K. Vishnoi, Andrew Xu
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:5934-5981, 2024.

Abstract

This paper considers the scenario in which there are multiple institutions, each with a limited capacity for candidates, and candidates, each with preferences over the institutions. A central entity evaluates the utility of each candidate to the institutions, and the goal is to select candidates for each institution in a way that maximizes utility while also considering the candidates’ preferences. The paper focuses on the setting in which candidates are divided into multiple groups and the observed utilities of candidates in some groups are biased–systematically lower than their true utilities. The first result is that, in these biased settings, prior algorithms can lead to selections with sub-optimal true utility and significant discrepancies in the fraction of candidates from each group that get their preferred choices. Subsequently, an algorithm is presented along with proof that it produces selections that achieve near-optimal group fairness with respect to preferences while also nearly maximizing the true utility under distributional assumptions. Further, extensive empirical validation of these results in real-world and synthetic settings, in which the distributional assumptions may not hold, are presented.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-celis24a, title = {Centralized Selection with Preferences in the Presence of Biases}, author = {Celis, L. Elisa and Kumar, Amit and Vishnoi, Nisheeth K. and Xu, Andrew}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {5934--5981}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/celis24a/celis24a.pdf}, url = {https://proceedings.mlr.press/v235/celis24a.html}, abstract = {This paper considers the scenario in which there are multiple institutions, each with a limited capacity for candidates, and candidates, each with preferences over the institutions. A central entity evaluates the utility of each candidate to the institutions, and the goal is to select candidates for each institution in a way that maximizes utility while also considering the candidates’ preferences. The paper focuses on the setting in which candidates are divided into multiple groups and the observed utilities of candidates in some groups are biased–systematically lower than their true utilities. The first result is that, in these biased settings, prior algorithms can lead to selections with sub-optimal true utility and significant discrepancies in the fraction of candidates from each group that get their preferred choices. Subsequently, an algorithm is presented along with proof that it produces selections that achieve near-optimal group fairness with respect to preferences while also nearly maximizing the true utility under distributional assumptions. Further, extensive empirical validation of these results in real-world and synthetic settings, in which the distributional assumptions may not hold, are presented.} }
Endnote
%0 Conference Paper %T Centralized Selection with Preferences in the Presence of Biases %A L. Elisa Celis %A Amit Kumar %A Nisheeth K. Vishnoi %A Andrew Xu %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-celis24a %I PMLR %P 5934--5981 %U https://proceedings.mlr.press/v235/celis24a.html %V 235 %X This paper considers the scenario in which there are multiple institutions, each with a limited capacity for candidates, and candidates, each with preferences over the institutions. A central entity evaluates the utility of each candidate to the institutions, and the goal is to select candidates for each institution in a way that maximizes utility while also considering the candidates’ preferences. The paper focuses on the setting in which candidates are divided into multiple groups and the observed utilities of candidates in some groups are biased–systematically lower than their true utilities. The first result is that, in these biased settings, prior algorithms can lead to selections with sub-optimal true utility and significant discrepancies in the fraction of candidates from each group that get their preferred choices. Subsequently, an algorithm is presented along with proof that it produces selections that achieve near-optimal group fairness with respect to preferences while also nearly maximizing the true utility under distributional assumptions. Further, extensive empirical validation of these results in real-world and synthetic settings, in which the distributional assumptions may not hold, are presented.
APA
Celis, L.E., Kumar, A., Vishnoi, N.K. & Xu, A.. (2024). Centralized Selection with Preferences in the Presence of Biases. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:5934-5981 Available from https://proceedings.mlr.press/v235/celis24a.html.

Related Material