Scalable Explainability-as-a-Service (XaaS) for Edge AI Systems

Explainability can be made more efficient by separating it from AI predictions: Traditional approaches generate explanations at the same time as AI decisions, which wastes computational resources. This paper proposes treating explanations as a separate service that can be requested on-demand, reducing processing time by 38% across real-world applications.

Cached explanations can be reused across similar situations: The XaaS system stores previously generated explanations and retrieves them for similar cases using semantic similarity matching, significantly cutting down redundant computation—a practical approach for resource-constrained edge devices like those in manufacturing or autonomous vehicles.

Explanations need verification mechanisms to maintain trustworthiness: The paper introduces a lightweight verification protocol to ensure that both newly generated and cached explanations remain accurate and faithful to the underlying AI model, addressing a key concern for responsible AI deployment in critical applications.

One-size-fits-all explanations don't work for diverse devices and users: The adaptive explanation engine adjusts which explanation methods are used based on what each device can handle and what users actually need, recognizing that edge AI systems operate across highly varied hardware and use cases (manufacturing, healthcare, autonomous vehicles).

Responsible AI at the edge requires system-level thinking: Rather than bolting explainability onto individual models, treating it as a distributed service enables better scalability, reliability, and resource management—important considerations for deploying trustworthy AI systems across many devices simultaneously.