{
  "id": 557,
  "url": "https://arxiv.org/abs/2606.27005v1",
  "title": "Adaptive Utility driven Resource Orchestration for Resilient AI (AURORA-AI)",
  "summary": "Modern AI systems are increasingly deployed under non-stationary computational, demographic, and operational conditions in which static resource allocation strategies degrade both predictive performance and human-centric properties such as fairness and explainability. This paper presents AURORA-AI, an Adaptive Utility-driven Resource Orchestration framework for Resilient AI that unifies Hamilton-Jacobi-Bellman feedback control, Lyapunov-based stability monitoring, and a fairness-aware composite ",
  "authors": "Rahul Umesh Mhapsekar, Ilias Cherkaoui, Lizy Abraham, Indrakshi Dey",
  "category": "research",
  "topics": "bias-fairness,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-25T13:22:07.000Z",
  "fetched_at": "2026-07-14T14:14:37.248Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/557",
  "original_url": "https://arxiv.org/abs/2606.27005v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}