{
  "id": 19116,
  "url": "https://arxiv.org/abs/2608.12669",
  "title": "From Fair Representation to Just Recognition in Generative AI",
  "summary": "arXiv:2608.12669v1 Announce Type: new Abstract: The fair AI/ML literature has long distinguished distributive fairness, concerning how automated systems allocate resources and opportunities, from representational fairness, concerning how they shape the ways individuals and social groups are perceived, understood, and accorded social status. Generative AI is rebalancing these normative dimensions. Unlike predictive systems, large language models (LLMs) and related technologies are fundamentally e",
  "authors": "Severin Engelmann, Daniel Susser",
  "category": "research",
  "topics": "bias-fairness",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-14T04:00:00.000Z",
  "fetched_at": "2026-08-14T05:10:49.168Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/19116",
  "original_url": "https://arxiv.org/abs/2608.12669",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}