{
  "id": 14870,
  "url": "https://arxiv.org/abs/2607.27232",
  "title": "Sympathetic Framing: Evaluating AI Alignment across Sociodemographic Groups",
  "summary": "arXiv:2607.27232v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly shaping how we consume information and form our worldview. This raises concerns beyond bias in AI: do LLMs grasp the emotional nuances conveyed via textual framing? In this work, we empirically evaluate how well an array of LLMs aligns with human emotional perception. Considering news headlines covering political and geopolitical conflicts, both human participants (n = 3011, a representative sample of",
  "authors": "Haran Shani-Narkiss, Michael Fire, Oren Tsur",
  "category": "research",
  "topics": "bias-fairness,safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-31T04:00:00.000Z",
  "fetched_at": "2026-07-31T05:10:57.675Z",
  "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/14870",
  "original_url": "https://arxiv.org/abs/2607.27232",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}