The Paradox of Prioritization in Public Sector Algorithms

TL;DR

This paper examines how the structural design of public sector prioritization algorithms can unintentionally increase disparities and perceptions of inequality, especially under resource scarcity.

Contribution

It reveals the fallibility of prioritization mechanisms in public agencies and highlights the risks of conflating efficiency with fairness in resource allocation.

Findings

Prioritization mechanisms generate significant disparities between intersectional groups.

Resource scarcity amplifies inequalities and perceptions of unfairness.

Algorithmic efficiency does not necessarily translate to equitable outcomes.

Abstract

Public sector agencies perform the critical task of implementing the redistributive role of the State by acting as the leading provider of critical public services that many rely on. In recent years, public agencies have been increasingly adopting algorithmic prioritization tools to determine which individuals should be allocated scarce public resources. Prior work on these tools has largely focused on assessing and improving their fairness, accuracy, and validity. However, what remains understudied is how the structural design of prioritization itself shapes both the effectiveness of these tools and the experiences of those subject to them under realistic public sector conditions. In this study, we demonstrate the fallibility of adopting a prioritization approach in the public sector by showing how the underlying mechanisms of prioritization generate significant relative disparities between groups of intersectional identities as resources become increasingly scarce. We argue that despite prevailing arguments that prioritization of resources can lead to efficient allocation outcomes, prioritization can intensify perceptions of inequality for impacted individuals. We contend that efficiencies generated by algorithmic tools should not be conflated with the dominant rhetoric that efficiency necessarily entails "doing more with less" and we highlight the risks of overlooking resource constraints present in real-world implementation contexts.