Explainable Artificial Intelligence has emerged as one of the most critical research directions in contemporary artificial intelligence, driven by the increasing deployment of complex machine learning systems in high-stakes social, economic, and institutional domains. As AI models become more powerful, opaque, and autonomous, the inability of stakeholders to understand, interpret, and contest algorithmic decisions raises profound technical, ethical, and governance-related concerns. This research article provides a comprehensive and theoretically grounded exploration of Explainable Artificial Intelligence, drawing strictly from established scholarly works that define, categorize, and critique explanation methods for black-box models, human-centered interpretability frameworks, ethical implications, and applied use cases such as credit scoring, cybersecurity, and risk management. Through extensive conceptual elaboration, the article examines how explanation techniques function not merely as technical artifacts but as socio-technical instruments that mediate trust, accountability, fairness, and moral responsibility. The methodological approach is qualitative and analytical, synthesizing taxonomies, philosophical perspectives, and applied frameworks to construct an integrative understanding of XAI as both a scientific and ethical endeavor. The results highlight recurring patterns across domains, demonstrating that explanation quality is deeply contingent on context, user expertise, and institutional purpose. The discussion critically evaluates limitations, including cognitive overload, explanation misuse, and the tension between model performance and interpretability, while outlining future research trajectories aimed at responsible AI governance. The study concludes that Explainable Artificial Intelligence is not an optional enhancement but a foundational requirement for aligning artificial intelligence systems with human values, democratic oversight, and sustainable technological progress.

Explainable Artificial Intelligence, Interpretability, Ethical AI, Black-Box Models

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