REVIEW AND ANALASYS OF EXPLAINABLE ARTIFICIAL INTELLIGENCE APPLICATIONS IN DISTRIBUTED SYSTEMS AND MICROSERVICES

The widespread use of AI systems in important infrastructure, it's very important that algorithmic decision-making processes are clear and easy to understand. Explainable Artificial Intelligence (XAI) has emerged as a vital research domain addressing the opacity in contemporary machine learning models, particularly deep neural networks, which function as inscrutable black boxes. This literature review provides a comprehensive examination of XAI methodologies and their applications in distributed systems, with a particular emphasis on microservice architectures. The review compiles contemporary taxonomies of XAI methods and elucidates the fundamental strategies that facilitate the comprehension of models across various computing environments. This study also critically examines the specific challenges posed by distributed microservice systems. The complexity of communication between services, the spread of faults, and the difficulty of observing the system all require new approaches to explainability. This review analyzes and categorizes existing methodologies, exposing considerable shortcomings in XAI aggregation, fidelity, and scalability in distributed settings. It suggests specific fixes, like using causal graphs to combine explanations from different services and making sidecar explanation interfaces the same across all services.