The accelerated adoption of artificial intelligence in socially sensitive and high-risk domains has intensified scholarly and institutional concern regarding the opacity, accountability, and ethical legitimacy of algorithmic decision-making. While modern machine learning systems, particularly deep learning architectures, demonstrate remarkable predictive and classificatory performance, they often do so at the cost of human comprehensibility. This growing disconnect between algorithmic capability and human understanding has positioned Explainable Artificial Intelligence as a central research paradigm aimed at restoring transparency, interpretability, and trust in AI-driven systems. This article presents a comprehensive and original scholarly examination of Explainable AI grounded exclusively in established academic literature, with particular emphasis on healthcare, financial risk assessment, and broader socio-technical systems. Rather than offering a superficial overview, the study engages in extensive theoretical elaboration, exploring how explainability functions as a cognitive, ethical, and institutional requirement rather than a purely technical feature. Through a qualitative synthesis of interdisciplinary research, the article examines the evolution of explainability from early interpretable models to contemporary post-hoc explanation frameworks, analyzing their philosophical assumptions, practical benefits, and inherent limitations. The findings reveal that explainability is deeply contextual, shaped by human expectations, regulatory environments, and domain-specific epistemologies. The discussion critically addresses counterarguments that question the feasibility and desirability of explainability, including concerns about oversimplification, false reassurance, and strategic misuse. By situating Explainable AI within a broader framework of trustworthy and human-centered artificial intelligence, the article argues that meaningful explainability requires not only algorithmic techniques but also institutional governance, user education, and ethical reflexivity. The paper concludes by outlining future research trajectories necessary to advance Explainable AI as a mature scientific discipline capable of supporting responsible AI deployment in complex real-world environments.

Explainable Artificial Intelligence, Algorithmic Accountability, Human-Centered AI, Interpretability

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