{
  "id": 14509,
  "url": "https://arxiv.org/abs/2607.26062",
  "title": "Identifying Implicit Bias in LLM-based Chat AI Toward People with Intellectual Disabilities",
  "summary": "arXiv:2607.26062v1 Announce Type: new Abstract: Background: This work investigates the presence of implicit bias in Large Language Model (LLM)-based chat AI models directed toward people with intellectual disabilities (ID). Objective: The study aims to identify and measure representational differences related to people with ID and examine them to identify implicit biases inherent in AI chat generation technologies. Methods: Utilizing the GPT-4-Turbo model, we requested story-generation based on",
  "authors": "Karly V. Coffey, Gloria L. Krahn, John P. Hanley, Jacob E. Neely",
  "category": "research",
  "topics": "bias-fairness,finance-investment",
  "orgs": "openai",
  "regions": null,
  "published_at": "2026-07-30T04:00:00.000Z",
  "fetched_at": "2026-07-30T05:10:24.387Z",
  "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/14509",
  "original_url": "https://arxiv.org/abs/2607.26062",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}