Position: Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized

Shomik Jain, Kathleen Creel, Ashia Camage Wilson
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:21148-21169, 2024.

Abstract

Contrary to traditional deterministic notions of algorithmic fairness, this paper argues that fairly allocating scarce resources using machine learning often requires randomness. We address why, when, and how to randomize by offering a set of stochastic procedures that more adequately account for all of the claims individuals have to allocations of social goods or opportunities and effectively balances their interests.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-jain24a, title = {Position: Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized}, author = {Jain, Shomik and Creel, Kathleen and Wilson, Ashia Camage}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {21148--21169}, 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/jain24a/jain24a.pdf}, url = {https://proceedings.mlr.press/v235/jain24a.html}, abstract = {Contrary to traditional deterministic notions of algorithmic fairness, this paper argues that fairly allocating scarce resources using machine learning often requires randomness. We address why, when, and how to randomize by offering a set of stochastic procedures that more adequately account for all of the claims individuals have to allocations of social goods or opportunities and effectively balances their interests.} }
Endnote
%0 Conference Paper %T Position: Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized %A Shomik Jain %A Kathleen Creel %A Ashia Camage Wilson %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-jain24a %I PMLR %P 21148--21169 %U https://proceedings.mlr.press/v235/jain24a.html %V 235 %X Contrary to traditional deterministic notions of algorithmic fairness, this paper argues that fairly allocating scarce resources using machine learning often requires randomness. We address why, when, and how to randomize by offering a set of stochastic procedures that more adequately account for all of the claims individuals have to allocations of social goods or opportunities and effectively balances their interests.
APA
Jain, S., Creel, K. & Wilson, A.C.. (2024). Position: Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:21148-21169 Available from https://proceedings.mlr.press/v235/jain24a.html.

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