{
  "id": 14835,
  "url": "https://arxiv.org/abs/2607.26485v1",
  "title": "Parameterized Fair Resource Allocation under Diversity Constraints",
  "summary": "Resource allocation across multiple agent groups arises in many applications including e-commerce recommendation systems, housing assignment, and course allocation, and is commonly formulated as an optimization problem with diversity constraints to ensure group fairness. Existing approaches typically enforce these constraints as hard conditions, which overly restrict the feasible solution space and often lead to suboptimal allocations. In this paper, we propose PRA, a parameterized framework for",
  "authors": "Keke Huang, Yik Yu Ng, Laks V. S. Lakshmanan, Xiaokui Xiao",
  "category": "research",
  "topics": "bias-fairness,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-29T05:25:02.000Z",
  "fetched_at": "2026-07-30T05:10:24.387Z",
  "source_slug": "x-arxiv-fairness-query",
  "source_name": "arXiv fairness query",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/14835",
  "original_url": "https://arxiv.org/abs/2607.26485v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}