Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized

Ashia Wilson; Kathleen Creel; Shomik Jain

Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized

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arxiv 2404.08592 v3 pith:W7LJRU3T submitted 2024-04-12 cs.CY

Shomik Jain , Kathleen Creel , Ashia Wilson This is my paper · ORCID

classification cs.CY

keywords allocationslearningmachinescarceaccountaddressadequatelyalgorithmic

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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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