Abstract

Abstract: As artificial intelligence (AI) systems become increasingly complex and pervasive, the need for transparency and interpretability has never been more critical. Explainable AI (XAI) addresses this need by providing methods and techniques to make AI decisions more understandable to humans. This paper explores the core principles of XAI, highlighting its importance for trust, accountability, and ethical AI deployment. We examine various XAI techniques, including interpretable models and post-hoc explanation methods, and discuss their strengths and limitations. Additionally, we present case studies demonstrating the practical applications of XAI across diverse domains such as healthcare, finance, and autonomous systems. The paper also addresses the ongoing challenges and outlines future research directions aimed at enhancing the effectiveness and applicability of XAI. By bridging the gap between complex AI systems and human understanding, XAI plays a pivotal role in fostering more reliable and responsible AI technologies.