{
  "id": 515,
  "url": "https://arxiv.org/abs/2606.28510v1",
  "title": "Generative AI Literacy Training Improves Intelligence Analysts' Discrimination of Real and AI-Generated Images",
  "summary": "Across social and online platforms, people are increasingly exposed to AI-generated images. As a consequence, the task of distinguishing AI-generated from authentic images is becoming a central challenge for information ecosystems. While humans perform better than chance, accuracy falls short of many operational needs. Initial evidence shows that visually oriented training can improve deepfake detection but does not improve participants' ability to identify real images as real. Here, we investig",
  "authors": "Negar Kamali, Candice Rockell Gerstner, Jessica Hullman, Matthew Groh",
  "category": "research",
  "topics": "bias-fairness,misinformation,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-26T18:05:08.000Z",
  "fetched_at": "2026-07-14T14:14:37.245Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/515",
  "original_url": "https://arxiv.org/abs/2606.28510v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}