Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized

2024/04/12 by Shomik Jain, Kathleen Creel, Jain, Shomik +3 · 4 citations
Computer Science · #Computers and Society (cs.CY) #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #K.4.0

paper · pdf · doi:10.48550/arxiv.2404.08592

openalex publication_date 2024/04/12 · openalex created_date 2024/04/16 · openalex updated_date 2026/07/28

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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