{
  "id": 697,
  "url": "https://arxiv.org/abs/2606.23380v2",
  "title": "Affective AI Safety: The Missing Piece in LLM Safety",
  "summary": "AI safety research has focused predominantly on epistemic and physical harms (e.g., misinformation, bias, system reliability) while the risks that arise from AI systems' engagement with human emotional life have remained fragmented and undertheorised. We propose affective safety as a unified class of AI safety concerns grounded in the fact that humans are affective beings. We develop a taxonomy of affective harms and identify recurring harm types: (1) affective self-alienation, (2) fairness and ",
  "authors": "Carolin Ifländer, Alba Curry, Flor Miriam Plaza-del-Arco, Amanda Cercas Curry",
  "category": "research",
  "topics": "bias-fairness,safety-alignment,misinformation",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-22T14:10:27.000Z",
  "fetched_at": "2026-07-14T14:14:41.554Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/697",
  "original_url": "https://arxiv.org/abs/2606.23380v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}