Explainable Artificial Intelligence has emerged as one of the most critical and contested domains in contemporary artificial intelligence research, driven by the increasing deployment of complex machine learning systems in high-stakes social, economic, and scientific contexts. This article develops a comprehensive, theory-driven, and historically grounded examination of explainable artificial intelligence by integrating foundational work from expert systems, fuzzy logic, neural-symbolic reasoning, Bayesian explanations, recommender systems, and modern deep learning interpretability techniques. Drawing strictly upon established scholarly references, the study synthesizes multiple generations of explanation paradigms, tracing their evolution from rule-based transparency and linguistic reasoning to gradient-based visual localization and human-centered explanation frameworks. The article articulates a unified conceptual model that positions explainability not merely as a technical add-on but as a socio-cognitive bridge between artificial systems and human understanding. A qualitative methodological synthesis is employed to analyze explanation mechanisms across symbolic, probabilistic, and sub-symbolic systems, revealing enduring design tensions between fidelity, usability, trust, and epistemic validity. The results highlight recurring explanatory structures, including causal attribution, contrastive reasoning, abstraction control, and contextual relevance, demonstrating their persistence across decades of artificial intelligence research. The discussion critically examines limitations related to scalability, cognitive overload, domain specificity, and ethical accountability, while also outlining future research directions that emphasize interdisciplinary integration, domain-sensitive explanation design, and human-in-the-loop evaluation. By offering an extensive theoretical elaboration grounded in canonical literature, this article contributes a publication-ready reference framework for scholars, designers, and policymakers seeking to advance explainable artificial intelligence as both a scientific discipline and a practical necessity.

Explainable Artificial Intelligence, Interpretability, Human-AI Interaction, Expert Systems

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