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The Paradox of Prioritization in Public Sector Algorithms

Erina Seh-Young Moon, Shion Guha

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.

The Paradox of Prioritization in Public Sector Algorithms

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.

Paper Structure

This paper contains 21 sections, 21 equations, 2 figures.

Table of Contents

  1. Introduction
  2. Related Works
  3. Two Prioritization Approaches in the Public Sector
  4. Hierarchical Prioritization
  5. Weighted Prioritization
  6. Structural Comparisons Between the Two Approaches
  7. Intra-category Tensions
  8. Inter-category Tensions
  9. Disparate Impact on Intersectional identities
  10. Metrics to measure disparate impact
  11. Hierarchical Prioritization can Produce Explosive Subgroup Disparities in High Resource Scarcity
  12. Weighted Prioritization Avoids Explosive Subgroup Disparities
  13. Visualizing Resource Allocation Disparities between Subgroups: An Example in Homelessness
  14. Discussion
  15. Limitations and Future Work
  16. ...and 6 more sections

Figures (2)

  • Figure 1: Two Types of Prioritization Approaches for Resource Allocation: A strict hierarchical prioritization approach (1st row) and weighted prioritization approach (second row). Categories are composed of heterogeneous groups of individuals of diverse attributes
  • Figure 2: Comparing Subgroup Resource Receipt Rates between Two Unhoused Groups, Families and Single Adults for Weighted and Hierarchical Prioritization: (Top-Left) Population distribution across categories. (Top-Right) Subgroup resource receipt rate ($G_s$) comparisons as a function of total population served. (Bottom-Left) Absolute Difference ($AD(B)$) in subgroup resource receipt rates between refugees and non-refugees. (Bottom-Right) Log-Ratio differences ($\ln(RD(B))$) in subgroup resource receipt rates between refugees and non-refugees

Theorems & Definitions (5)

  • Definition 3.1: Hierarchical Prioritization
  • Definition 3.2: Weighted Prioritization
  • Definition 5.1: Subgroup Resource Receipt Rate
  • Definition 5.2: Absolute Difference in Subgroup Resource Receipt Rate
  • Definition 5.3: Log-Ratio Difference in Subgroup Resource Receipt Rate