Researchers have introduced Explainability-as-a-Service (XaaS), a novel distributed architecture designed to make AI explanations more efficient and scalable for edge devices. This system decouples explanation generation from model inference, allowing edge devices to request and cache explanations based on resource constraints. Key innovations include a distributed cache with semantic retrieval, a lightweight verification protocol, and an adaptive engine for selecting explanation methods. Evaluations on manufacturing, autonomous vehicles, and healthcare use cases demonstrated a 38% reduction in latency while maintaining explanation quality. AI
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IMPACT Enables more transparent and accountable AI deployment across large-scale, heterogeneous IoT systems.
RANK_REASON Academic paper proposing a new system architecture for AI explainability.