{
  "id": 17900,
  "url": "https://arxiv.org/abs/2608.07091v1",
  "title": "Human-Centered Explainable AI for TinyML Edge Devices: A Pareto-Based Selection Framework with LLM-Guided Design",
  "summary": "Edge Artificial Intelligence (Edge AI) enables the deployment of AI models directly on local edge devices, while such deployments are subject to strict resource constraints, particularly in clinical applications requiring local and timely inference. In such contexts, explainable artificial intelligence (XAI) can serve as a human-AI interface intended to support healthcare professionals' and patients' understanding of model predictions and informed decision-making. To fulfill this role, XAI metho",
  "authors": "Zeinab Dehghani, Dhavalkumar Thakker, Koorosh Aslansefat, Kuniko Paxton, Bhupesh Kumar Mishra, Baseer Ahmad, Rameez Raja Kureshi",
  "category": "research",
  "topics": "healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-07T10:40:53.000Z",
  "fetched_at": "2026-08-10T05:10:00.488Z",
  "source_slug": "x-arxiv-cs-hc",
  "source_name": "arXiv cs.HC",
  "source_homepage": "https://arxiv.org/list/cs.HC/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/17900",
  "original_url": "https://arxiv.org/abs/2608.07091v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}