Explainable AI (XAI): A Survey on Interpretability Techniques and Real-World Applications

Abstract

As artificial intelligence systems become increasingly complex, the demand for explainability and transparency has grown. This review provides a comprehensive analysis of Explainable AI (XAI) techniques, including feature attribution methods, surrogate models, and inherently interpretable architectures. We discuss the importance of interpretability in high-stakes applications such as healthcare, finance, and law enforcement. The paper also examines existing challenges, such as the trade-off between accuracy and explainability, regulatory compliance, and user trust. We conclude with future research directions to enhance the transparency and accountability of AI systems.