# Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized

arXiv: https://arxiv.org/abs/2404.08592 · HTML: https://mlsanity.com/paper/2404.08592/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2024.

## Built on by (most cited first, 7 of 7)

- [Correlated Errors in Large Language Models](https://mlsanity.com/paper/2506.07962/) (2025)
- [Systemizing Multiplicity: The Curious Case of Arbitrariness in Machine Learning](https://mlsanity.com/paper/2501.14959/) (2025)
- [Learning treatment effects while treating those in need](https://mlsanity.com/paper/2407.07596/) (2024)
- [The Value of Prediction in Identifying the Worst-Off](https://mlsanity.com/paper/2501.19334/) (2025)
- [Unequal Uncertainty: Rethinking Algorithmic Interventions for Mitigating Discrimination from AI](https://mlsanity.com/paper/2508.07872/) (2025)
- [Data as a Lever: A Neighbouring Datasets Perspective on Predictive Multiplicity](https://mlsanity.com/paper/2510.21303/) (2025)
- [On the Meta-Design of Allocation Problems](https://mlsanity.com/paper/2602.08786/) (2026)

## Abstract (arXiv)

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.