Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized

Shomik Jain, Kathleen A. Creel, Ashia Wilson
2/12/2026

No official code found. Best implementation: shomikj/randomization_for_fairness (2 stars, last commit 2024-06-28).

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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Code Implementations(1)

shomikj/randomization_for_fairnessCommunity100%

Experiments for the ICML 2024 Paper: Scarce Resource Allocations That Rely on ML Should Be Randomized

20Python, Jupyter NotebookMay 21, 20242 years ago

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Cite this paper

@article{jain2026scarce,
  title  = {Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized},
  author = {Shomik Jain and Kathleen A. Creel and Ashia Wilson},
  year   = {2026},
  eprint = {2404.08592},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2404.08592},
  journal = {ICML 2024 2024}
}

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