{
  "id": 1968,
  "url": "https://link.springer.com/article/10.1007/s11023-026-09788-7",
  "title": "Problem of (In)Explainability in Testing Fully Autonomous Weapon Systems for International Humanitarian Law Compliance",
  "summary": "Fully autonomous weapon systems need to comply with International Humanitarian Law and underlying ethical principles. This requires the ability to recognize not only objects or persons to be targeted but also protected persons or objects. Such sophisticated object classification abilities, if achievable at all, would have to utilize machine learning techniques. These come with well-known limitations to predictability, reliability and explainability. This article argues such limitations could be ",
  "authors": null,
  "category": "research",
  "topics": "regulation,military-security,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-10T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:11:46.979Z",
  "source_slug": "x-minds-and-machines",
  "source_name": "Minds and Machines",
  "source_homepage": "https://link.springer.com/journal/11023",
  "ethics_ai_record_url": "https://ethics.ai/record/1968",
  "original_url": "https://link.springer.com/article/10.1007/s11023-026-09788-7",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}