Explainable Artificial Intelligence: Techniques, Challenges, and Applications in Critical Decision-Making Systems

Abstract

The proliferation of artificial intelligence (AI) and machine learning (ML) models into safety-critical domains such as healthcare, finance, and autonomous systems has exposed a significant challenge: the inherent “black-box” nature of many state-of-the-art algorithms. This lack of transparency can impede user trust, create accountability gaps, and mask underlying biases, posing substantial risks in high-stakes decision-making environments. Explainable AI (XAI) has emerged as a critical field of research dedicated to developing techniques that render AI decisions more understandable to humans. This paper provides a comprehensive overview of the XAI landscape, examining its fundamental principles, a taxonomy of current methods, and a detailed analysis of prominent techniques, including Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). We explore the practical application of these methods in critical domains, highlighting their role in enhancing clinical decision support, ensuring fairness in financial services, and promoting safety in autonomous vehicles. Furthermore, the paper discusses the persistent challenges facing the field, such as the trade-off between model performance and interpretability, the lack of standardized evaluation metrics, and the human-centric aspects of explanation. Finally, we outline future research directions aimed at building more robust, reliable, and trustworthy AI systems.