UNPACKING EXPLAINABLE AI (XAI): BRIDGING THE GAP BETWEEN AI MODELS AND HUMAN UNDERSTANDING

Abstract

Explainable AI (XAI) has emerged as a crucial bridge between complex artificial intelligence systems and human understanding, addressing the growing need for transparency in AI-driven decision-making. This article presents a comprehensive analysis of XAI methodologies, focusing on key approaches including SHAP (SHapley Additive explanations), LIME (Local Interpretable Model-agnostic Explanations), and counterfactual explanations. This article examines how these methods contribute to making AI systems more interpretable across various sectors, from healthcare diagnostics to financial services. Through an extensive review of current literature and real-world applications, this article demonstrates how XAI facilitates trust-building, regulatory compliance, and ethical AI deployment while maintaining model performance. This article reveals the critical role of stakeholder-specific explanations and industry-specific implementation strategies in successful XAI adoption. This article concludes by identifying emerging challenges and future research directions in the field, emphasizing the importance of balancing transparency with model sophistication. By synthesizing insights from leading researchers, this article provides a comprehensive framework for understanding and implementing XAI solutions across diverse applications.