Fairness in Contextual Resource Allocation Systems: Metrics and Incompatibility Results
Nathanael Jo, Bill Tang, Kathryn Dullerud, Sina Aghaei, Eric Rice,, Phebe Vayanos

TL;DR
This paper introduces a framework for evaluating and understanding fairness in resource allocation systems, revealing fundamental incompatibilities between different fairness metrics and guiding policy design.
Contribution
It proposes a novel fairness evaluation framework inspired by machine learning metrics and presents key incompatibility results among fairness criteria in resource allocation.
Findings
Fairness in allocation and outcomes are often incompatible.
Vulnerability-based policies can lead to outcome disparities.
Using additional contextual information can improve fairness.
Abstract
We study critical systems that allocate scarce resources to satisfy basic needs, such as homeless services that provide housing. These systems often support communities disproportionately affected by systemic racial, gender, or other injustices, so it is crucial to design these systems with fairness considerations in mind. To address this problem, we propose a framework for evaluating fairness in contextual resource allocation systems that is inspired by fairness metrics in machine learning. This framework can be applied to evaluate the fairness properties of a historical policy, as well as to impose constraints in the design of new (counterfactual) allocation policies. Our work culminates with a set of incompatibility results that investigate the interplay between the different fairness metrics we propose. Notably, we demonstrate that: 1) fairness in allocation and fairness in outcomes…
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Taxonomy
TopicsIncome, Poverty, and Inequality
