{
  "id": 18806,
  "url": "https://arxiv.org/abs/2608.11491v1",
  "title": "The Accuracy Trap: Structural Scarcity Amplifies Relative Inequality in Algorithmic Allocation",
  "summary": "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 classifi",
  "authors": "Erina Seh-Young Moon, Matthew Tamura, Shion Guha",
  "category": "research",
  "topics": "bias-fairness,children-education",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-11T23:04:43.000Z",
  "fetched_at": "2026-08-13T05:10:37.786Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/18806",
  "original_url": "https://arxiv.org/abs/2608.11491v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}