The Paradox of Prioritization in Public Sector Algorithms

2026-04-02

AI Summary

This study addresses how algorithmic prioritization in public-sector resource allocation under scarcity can exacerbate relative inequalities among intersectional identity groups and erode public perceptions of institutional fairness. By integrating fairness analysis, intersectionality theory, and policy implementation modeling within real-world constraints, the research systematically uncovers the adverse effects of algorithmic prioritization mechanisms driven by an “efficiency-first” logic. Findings reveal that greater resource scarcity intensifies the inequality-amplifying effects of such algorithms and strengthens individuals’ perceptions of systemic injustice. These results challenge the prevailing narrative that equates technical efficiency with optimal allocation of public resources, highlighting the need to critically reassess algorithmic design in policy contexts where equity and legitimacy are paramount.

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.

Problem

• algorithmic prioritization
• public sector
• resource scarcity
• intersectional disparities
• perceived inequality

Innovation

• algorithmic prioritization
• public sector algorithms
• intersectional disparities
• resource scarcity
• efficiency paradox