EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI): BRIDGING TRANSPARENCY AND TRUST IN MACHINE LEARNING SYSTEMS

Abstract

Explainable Artificial Intelligence (XAI) addresses the opacity of complex machine learning models by enhancing transparency, interpretability, and trust in AI systems. This paper explores the fundamental principles of XAI, including transparency, accountability, and fairness, while delineating the need for explainability in high-stakes domains such as healthcare, finance, and autonomous systems. It categorizes XAI methods into model-specific and model-agnostic techniques, such as LIME and SHAP, and examines their real-world applications for improving decision-making, regulatory compliance, and ethical AI deployment. Challenges like balancing accuracy with interpretability, user-specific explanations, and standardization of metrics are discussed, alongside future directions emphasizing human-centered design. Through a comprehensive review, the paper underscores XAI's role in fostering responsible AI adoption, mitigating biases, and bridging the gap between advanced AI performance and human understanding.