{
  "id": 6371,
  "url": "https://arxiv.org/abs/2604.03533v1",
  "title": "Automated Analysis of Global AI Safety Initiatives: A Taxonomy-Driven LLM Approach",
  "summary": "We present an automated crosswalk framework that compares an AI safety policy document pair under a shared taxonomy of activities. Using the activity categories defined in Activity Map on AI Safety as fixed aspects, the system extracts and maps relevant activities, then produces for each aspect a short summary for each document, a brief comparison, and a similarity score. We assess the stability and validity of LLM-based crosswalk analysis across public policy documents. Using five large languag",
  "authors": "Takayuki Semitsu, Naoto Kiribuchi, Kengo Zenitani",
  "category": "research",
  "topics": "regulation,safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-04T01:02:15.000Z",
  "fetched_at": "2026-07-14T16:32:28.608Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6371",
  "original_url": "https://arxiv.org/abs/2604.03533v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}