Background: Artificial intelligence has achieved unprecedented predictive and decision-making capabilities across diverse domains such as healthcare, finance, energy systems, civil engineering, and organizational management. However, the increasing opacity of complex machine learning and deep learning models has raised critical concerns regarding trust, accountability, fairness, and regulatory compliance. Explainable Artificial Intelligence (XAI) has emerged as a pivotal paradigm aimed at addressing these concerns by rendering AI systems transparent, interpretable, and human-understandable.
Objective: This research develops a comprehensive, domain-transcendent theoretical and applied framework for explainable artificial intelligence by synthesizing insights from multidisciplinary applications including medical diagnostics, financial risk management, energy forecasting, structural engineering, organizational agility prediction, and counterfactual reasoning. The study seeks to identify unifying principles, methodological patterns, and conceptual gaps that limit the scalability and reliability of XAI systems across real-world settings.
Methods: A qualitative, theory-driven research methodology is employed, grounded strictly in an in-depth analytical synthesis of contemporary peer-reviewed literature on XAI. The methodology integrates interpretability taxonomies, post-hoc and intrinsic explanation strategies, counterfactual reasoning mechanisms, and self-explainable model architectures. Emphasis is placed on descriptive methodological reasoning rather than mathematical formalization, aligning with interdisciplinary accessibility requirements.
Results: The findings reveal that while XAI techniques demonstrate significant domain-specific effectiveness, they remain fragmented in conceptual alignment and evaluation standards. Medical and biological applications prioritize causal and feature-attribution explanations, finance emphasizes transparency and regulatory compliance, energy systems focus on temporal explainability, and engineering domains demand structural logic validation. A unifying theoretical scaffold based on explanation purpose, stakeholder cognition, and decision risk is identified.
Conclusion: The study concludes that future progress in XAI depends on transitioning from tool-centric explanations to cognition-aware, context-sensitive, and ethically grounded explanatory ecosystems. The proposed unified framework advances explainable AI beyond interpretability toward actionable trust, supporting responsible deployment across high-stakes domains.

Explainable Artificial Intelligence, Trustworthy AI,, Interpretability, Counterfactual Explanations

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