The Role of Explainable AI (XAI) in High-Stakes Decision-Making

Abstract

As AI systems increasingly influence high-stakes domains such as healthcare, finance, and criminal justice, the need for transparency and interpretability has become critical. This paper explores Explainable AI (XAI) techniques that enhance the interpretability of machine learning models without compromising accuracy. We discuss various XAI methods, including SHAP (Shapley Additive Explanations), LIME (Local Interpretable Model-agnostic Explanations), and counterfactual explanations, and evaluate their effectiveness in real-world applications. Ethical considerations, regulatory requirements, and future directions for responsible AI deployment are also examined.