Resource-constrained Fairness

TL;DR

This paper introduces 'resource-constrained fairness,' a framework that accounts for limited resources in fair machine learning, highlighting how resource availability impacts fairness costs and addressing a gap in existing tools.

Contribution

It proposes a new fairness framework considering resource constraints and quantifies the associated fairness costs, which vary with resource levels.

Findings

Resource availability significantly affects fairness costs.

Existing fairness tools do not account for resource limitations.

Resource-constrained fairness provides a more realistic deployment assessment.

Abstract

Access to resources strongly constrains the decisions we make. While we might wish to offer every student a scholarship, or schedule every patient for follow-up meetings with a specialist, limited resources mean that this is not possible. When deploying machine learning systems, these resource constraints are simply enforced by varying the threshold of a classifier. However, these finite resource limitations are disregarded by most existing tools for fair machine learning, which do not allow the specification of resource limitations and do not remain fair when varying thresholds. This makes them ill-suited for real-world deployment. Our research introduces the concept of "resource-constrained fairness" and quantifies the cost of fairness within this framework. We demonstrate that the level of available resources significantly influences this cost, a factor overlooked in previous evaluations.