A Modular Framework for Decentralized Explainable AI using Blockchain

Abstract

This paper unveils a pioneering modular framework for Decentralized Explainable Artificial Intelligence (DeXAI), harnessing blockchain to deliver unparalleled trust and clarity in AI systems. Addressing the opacity and privacy challenges of centralized AI, our framework integrates federated learning with Explainable AI (XAI) methods, namely SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), to produce intuitive explanations for AI decisions across distributed networks. A blockchain layer secures predictions and explanations, with smart contracts ensuring ethical compliance and auditable trails. Designed for adaptability, the framework supports diverse AI models and blockchain platforms, excelling in critical sectors like healthcare and finance. Our prototype validates its scalability and effectiveness, setting a new benchmark for trustworthy AI.