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

Research from arXiv highlights that algorithmic systems allocating scarce public resources, such as child welfare interventions or cancer treatment referrals, can inadvertently amplify relative inequality. Under conditions of 'structural scarcity' (where demand significantly outstrips supply), traditional fairness approaches focusing on data bias or model deficiencies are insufficient. The study introduces a scaling law demonstrating that relative disparity between groups increases exponentially with scarcity-induced thresholds and rank-discrimination fidelity, indicating that accuracy in ranking can exacerbate existing inequalities when resources are limited.

Why it matters

This research reveals a critical flaw in applying conventional fairness metrics to algorithmic allocation of scarce resources, demonstrating that even accurate systems can increase inequality. Understanding this 'accuracy trap' is vital for any institution utilizing AI for resource distribution, as it necessitates a re-evaluation of current fairness paradigms and algorithmic design principles. The findings underscore the need for strategies that address structural scarcity and its impact on equitable access, rather than solely focusing on model performance.

Key insights

• Algorithmic systems are increasingly used to rank individuals for access to scarce public resources.
• The prevailing fairness paradigm addresses disparity through data debiasing and model calibration.
• Under structural scarcity, where demand greatly exceeds supply, allocation becomes a rationing problem, altering the statistical properties of ranking from classification.
• A derived scaling law ($D ext{ } oldsymbol{ ext{∝}} ext{ } ext{exp}(t ext{ } oldsymbol{ ext{·}} ext{ } ho ext{ } oldsymbol{ ext{·}} ext{ } ext{Δ})$) indicates that relative disparity between groups grows exponentially with scarcity-induced thresholds and rank-discrimination fidelity.
• The research implies that higher algorithmic accuracy can lead to greater relative inequality in resource allocation under scarcity, a concept termed 'the accuracy trap'.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.11491