{
  "id": 606,
  "url": "https://arxiv.org/abs/2606.26200v1",
  "title": "Statistical and Structural Approaches to Algorithmic Fairness",
  "summary": "Modern machine learning systems have outgrown their origins as isolated predictive constructs, evolving into complex socio-technical architectures that actively mediate human opportunity. As algorithms increasingly determine access to economic and social opportunities, it has become widely recognized that these systems are deeply embedded with the structural inequalities and prejudices of their environments. The field of algorithmic fairness emerged in response to the growing recognition that mo",
  "authors": "Antonio Ferrara",
  "category": "research",
  "topics": "bias-fairness,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-24T16:23:40.000Z",
  "fetched_at": "2026-07-14T14:14:41.548Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/606",
  "original_url": "https://arxiv.org/abs/2606.26200v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}