The Accuracy Trap: Structural Scarcity Amplifies Relative Inequality in Algorithmic Allocation

2026-08-11

AI Summary

This study addresses how algorithmic allocation systems, despite optimizing for accuracy, can exacerbate intergroup inequality under conditions of structural resource scarcity. The authors propose the “accuracy trap” theory, demonstrating that in ranking tasks, the interaction between accuracy objectives and resource scarcity exponentially amplifies existing disparities—thereby transcending the limits of conventional fairness calibration frameworks. Through Monte Carlo simulations, mathematical modeling, and empirical analyses of real-world systems—including Canadian child welfare services and U.S. cancer care allocation—the paper validates the pervasive presence of this mechanism in public-sector contexts. The findings reveal that bias mitigation alone is insufficient to counteract the inequality-amplifying effects driven by structural scarcity.

Abstract

Algorithmic systems increasingly rank individuals for access to scarce public resources, from child welfare interventions to cancer treatment referrals. The prevailing fairness frame treats disparity as a property of biased data or deficient models, with remedies through calibration and debiasing. Under structural scarcity, where demand exceeds supply by an order of magnitude, allocation becomes a rationing problem, and the statistical properties of ranking diverge sharply from those of classification. We derive a scaling law $D \propto \exp(t \cdot ρ\cdot Δ)$, in which relative disparity between two groups separated by a structural gap $Δ$ grows in the product of the scarcity-induced threshold $t$ and rank-discrimination fidelity $ρ$. Scarcity and accuracy interact multiplicatively, producing exponentially larger between-group disparities. We term this dynamic the Accuracy Trap. We validate this Accuracy Trap through Monte Carlo simulation and two independent public-sector systems in Canadian child welfare and U.S. cancer care. Debiasing alone cannot dissolve the trap.

Problem

• algorithmic fairness
• structural scarcity
• resource allocation
• relative inequality
• ranking systems

Innovation

• Accuracy Trap
• structural scarcity
• algorithmic fairness
• ranking disparity
• scaling law