{
  "id": 6152,
  "url": "https://arxiv.org/abs/2604.07883v1",
  "title": "An Agentic Evaluation Architecture for Historical Bias Detection in Educational Textbooks",
  "summary": "History textbooks often contain implicit biases, nationalist framing, and selective omissions that are difficult to audit at scale. We propose an agentic evaluation architecture comprising a multimodal screening agent, a heterogeneous jury of five evaluative agents, and a meta-agent for verdict synthesis and human escalation. A central contribution is a Source Attribution Protocol that distinguishes textbook narrative from quoted historical sources, preventing the misattribution that causes syst",
  "authors": "Gabriel Stefan, Adrian-Marius Dumitran",
  "category": "research",
  "topics": "bias-fairness,agents-autonomy,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-09T06:51:32.000Z",
  "fetched_at": "2026-07-14T16:32:15.638Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6152",
  "original_url": "https://arxiv.org/abs/2604.07883v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}