Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized

TL;DR

This paper argues that fair allocation of scarce resources via machine learning should incorporate randomness, proposing stochastic procedures to better reflect individuals' claims and improve fairness.

Contribution

It introduces the idea that randomness in resource allocation can enhance fairness, providing new stochastic methods tailored for social good distributions.

Findings

Randomized procedures better capture individual claims.

Stochastic methods improve fairness in resource allocation.

The approach challenges deterministic fairness paradigms.

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.