BRIDGING THE GAP BETWEEN COMPLEXITY AND UNDERSTANDING: A COMPREHENSIVE STUDY ON EXPLAINABLE AI (XAI) AND ITS ROLE IN TRANSPARENT ARTIFICIAL INTELLIGENCE SYSTEMS

Explainable Artificial Intelligence (XAI) has emerged as a critical research area aimed at enhancing transparency, trust, and accountability in AI systems. As machine learning algorithms, particularly deep learning models, become increasingly complex, their decision-making processes often resemble "black boxes," leaving end-users and stakeholders in the dark about how certain outcomes are derived. This paper explores the significance of XAI, its methodologies, and its role in critical domains such as healthcare, finance, and autonomous systems. We analyze existing frameworks, discuss challenges in implementation, and present future directions for research and deployment.