{
  "id": 9769,
  "url": "https://doi.org/10.1007/s00146-025-02422-7",
  "title": "Exploring automation bias in human–AI collaboration: a review and implications for explainable AI",
  "summary": "Abstract As Artificial Intelligence (AI) becomes increasingly embedded in high-stakes domains such as healthcare, law, and public administration, automation bias (AB)—the tendency to over-rely on automated recommendations—has emerged as a critical challenge in human–AI collaboration. While previous reviews have examined AB in traditional computer-assisted decision-making, research on its implications in modern AI-driven work environments remains limited. To address this gap, this research system",
  "authors": "Giuseppe Romeo, Daniela Conti",
  "category": "research",
  "topics": "bias-fairness,regulation,jobs-economy,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2025-07-03T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:34:00.822Z",
  "source_slug": "openalex",
  "source_name": "OpenAlex",
  "source_homepage": "https://openalex.org",
  "ethics_ai_record_url": "https://ethics.ai/record/9769",
  "original_url": "https://doi.org/10.1007/s00146-025-02422-7",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}