{
  "id": 13076,
  "url": "https://arxiv.org/abs/2607.20695v1",
  "title": "Language Models Embody and Amplify Human Cognitive Distortions: What Is to Be Done?",
  "summary": "Human judgment is fundamentally prone to error. A promise of AI is that it will rid decisions of bias and ensure a fairer and safer world for all. Yet research unequivocally demonstrates that LLMs exhibit consequential sociocognitive biases. We alert readers that bias in AI (a) is covert and ironically a feature of alignment goals, (b) is not merely a mirror, but an amplifier of human bias, (c) intensifies across model generations, and (d) even transmits bias to humans. Given the potentially sei",
  "authors": "Arnau Marin-Llobet, Steven A. Lehr, Mahzarin R. Banaji",
  "category": "research",
  "topics": "bias-fairness,safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-22T19:53:51.000Z",
  "fetched_at": "2026-07-25T05:10:48.796Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/13076",
  "original_url": "https://arxiv.org/abs/2607.20695v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}