Human-Centered Explainable AI for TinyML Edge Devices: A Pareto-Based Selection Framework with LLM-Guided Design
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
Record details
Published: 7 August 2026
Source: arXiv cs.HC
Category: Research
Topics: Healthcare · Transparency
Retrieved: 10 August 2026
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ethics.ai (7 August 2026), “Human-Centered Explainable AI for TinyML Edge Devices: A Pareto-Based Selection Framework with LLM-Guided Design,” evidence record 17900, https://ethics.ai/record/17900 (originally published by arXiv cs.HC).
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