{
  "id": 13686,
  "url": "https://arxiv.org/abs/2607.22525v1",
  "title": "Explainable Reinforcement Learning for assisting Air Traffic Controllers",
  "summary": "To effectively integrate AI into high-stakes, critical environments such as healthcare, autonomous driving, and aviation--and to advance toward higher levels of automation and seamless human-AI collaboration--building trust in AI-driven solutions is essential. Trust, in turn, is closely linked to the explainability of AI systems. The rapid advancements in AI across various domains have underscored the challenges of establishing trust, raising increasing interest in AI explainability even more wh",
  "authors": "Anduel Mehmeti, Gabriella Gigante, Salvatore Venticinque",
  "category": "research",
  "topics": "jobs-economy,healthcare,transparency,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-24T17:56:38.000Z",
  "fetched_at": "2026-07-27T05:10:06.638Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/13686",
  "original_url": "https://arxiv.org/abs/2607.22525v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}