Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized

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.

To appear in the proceedings of the International Conference on Machine Learning (ICML 2024)