Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized

Shomik Jain; Kathleen Creel; Ashia Wilson

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arXiv · 2404.08592

Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized

Shomik Jain·Kathleen Creel·Ashia Wilson

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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 proposing stochastic procedures that more adequately account for all of the claims that individuals have to allocations of social goods or opportunities.

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Shomik Jain, Kathleen Creel, Ashia Wilson. 2024-04-12. Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized. https://arxiv.org/abs/2404.08592

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