A Comprehensive Review of Trust Governance, Explainable, and Sustainability Challenges in Edge-Driven Intelligent Systems
Keywords:
Carbon aware computing, Edge intelligence, Explainable artificial intelligence, Sustainable edge systems, Trust governanceAbstract
Edge-integrated intelligent systems have seen considerable progress in real-time distributed computing applications, and the current state of research, as presented in existing studies, shows considerable fragmentation in terms of trust governance, explainability, and sustainability. A systematic review of recent literature shows that close to 65% of edge AI research is concerned with performance and latency optimization, and less than 20% of the research includes formal trust governance or audit compliance. Similarly, although explainable artificial intelligence has seen considerable progress, more than 75% of the proposed solutions are cloud-centric. The sustainability studies are mostly concerned with hardware or network-level energy efficiency, and there is a lack of quantitative analysis of carbon-aware AI inference and lifecycle emissions in distributed edge systems. These findings point to the lack of comprehensive frameworks that combine governance-driven trust, lightweight explainable, and carbon-aware optimization. This review systematically points out the technological and architectural gaps and thus justifies the need for a trust-governed, explainable, and environmentally sustainable framework to facilitate the development of next-generation edge intelligence systems.
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