Human-Centered AI: The Role of Explainability in Modern AI Systems

Abstract: As artificial intelligence (AI) systems become increasingly complex and pervasive, the need for transparency andinterpretability has never been more critical. Explainable AI (XAI) addresses this need by providing methods and techniques to makeAI 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-hocexplanation methods, and discuss their strengths and limitations. Additionally, we present case studies demonstrating the practicalapplications of XAI across diverse domains such as healthcare, finance, and autonomous systems. The paper also addresses theongoing challenges and outlines future research directions aimed at enhancing the effectiveness and applicability of XAI. Bybridgingthe gap between complex AI systems and human understanding, XAI plays a pivotal role in fostering more reliable and responsibleAI technologies.