arXiv:2602.04120v3 Announce Type: replace 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 se
The proliferation of AI at the edge, particularly in IoT and specialized hardware, creates an urgent need for efficient explainability solutions that overcome computational and latency constraints.
This development addresses a critical barrier to widespread, trustworthy AI deployment in embedded systems, enabling better monitoring, debugging, and regulatory compliance for edge AI.
Explainable AI (XAI) transitions from ad-hoc, coupled approaches to a distributed, scalable 'as-a-service' model, optimizing resource use and improving deployability across diverse edge devices.
- · Edge AI providers
- · IoT device manufacturers
- · Developers of XAI services
- · Sectors requiring high reliability AI (e.g., industrial automation, healthcare)
- · Traditional, coupled XAI methods
- · Edge AI deployments without integrated explainability
- · Companies unable to adapt to distributed XAI architectures
Improved debugging and auditing of AI models on resource-constrained edge devices.
Increased adoption of AI in safety-critical edge applications due to enhanced trust and transparency.
The emergence of new regulatory frameworks specifically tailored for explainable edge AI systems, demanding XaaS-like implementations.
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Read at arXiv cs.LG