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

TL;DR

This paper introduces XaaS, a scalable distributed architecture that decouples explanation generation from inference in edge AI, reducing latency and redundancy while maintaining explanation quality across diverse IoT applications.

Contribution

The paper proposes a novel XaaS architecture with a distributed explanation cache, verification protocol, and adaptive engine, enabling efficient, scalable explainability for edge AI systems.

Findings

XaaS reduces explanation latency by 38% in real-world edge applications.

The architecture maintains high explanation fidelity across heterogeneous devices.

Decoupling inference from explanation generation improves scalability and resource efficiency.

Abstract

Though Explainable AI (XAI) has made significant advancements, its inclusion in edge and IoT systems is typically ad-hoc and inefficient. Most current methods are "coupled" in such a way that they generate explanations simultaneously with model inferences. As a result, these approaches incur redundant computation, high latency and poor scalability when deployed across heterogeneous sets of edge devices. In this work we propose Explainability-as-a-Service (XaaS), a distributed architecture for treating explainability as a first-class system service (as opposed to a model-specific feature). The key innovation in our proposed XaaS architecture is that it decouples inference from explanation generation allowing edge devices to request, cache and verify explanations subject to resource and latency constraints. To achieve this, we introduce three main innovations: (1) A distributed explanation cache with a semantic similarity based explanation retrieval method which significantly reduces redundant computation; (2) A lightweight verification protocol that ensures the fidelity of both cached and newly generated explanations; and (3) An adaptive explanation engine that chooses explanation methods based upon device capability and user requirement. We evaluated the performance of XaaS on three real-world edge-AI use cases: (i) manufacturing quality control; (ii) autonomous vehicle perception; and (iii) healthcare diagnostics. Experimental results show that XaaS reduces latency by 38% while maintaining high explanation quality across three real-world deployments. Overall, this work enables the deployment of transparent and accountable AI across large scale, heterogeneous IoT systems, and bridges the gap between XAI research and edge-practicality.