{
  "id": 4956,
  "url": "https://arxiv.org/abs/2605.03847v3",
  "title": "Mechanical Conscience: A Mathematical Framework for Dependability of Machine Intelligence",
  "summary": "Distributed collaborative intelligence (DCI), encompassing edge-to-edge architectures, federated learning, transfer learning, and swarm systems, creates environments in which emergent risk is structurally unavoidable: locally correct decisions by individual agents compose into globally unacceptable behavioral trajectories under uncertainty. Existing approaches such as constrained optimization, safe reinforcement learning, and runtime assurance evaluate acceptability at the level of individual ac",
  "authors": "Munkhdelgerekh Batzorig, Purevbaatar Ganbold, Kyungbin Park, Pilkong Jeong, Kangbin Yim",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-05T15:14:02.000Z",
  "fetched_at": "2026-07-14T16:31:21.936Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4956",
  "original_url": "https://arxiv.org/abs/2605.03847v3",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}