Explainable Artificial Intelligence (XAI): Techniques, Applications, Challenges and Future Directions - A Review

Abstract

Machine learning models, and deep neural networks in particular, now inform decisions in healthcare, finance, criminal justice, and other high-stakes domains, yet their internal reasoning remains largely opaque to the people who rely on their outputs. Explainable Artificial Intelligence (XAI) is the body of methods and design principles that render such black-box systems interpretable to developers, regulators, and end users without materially degrading predictive performance. This review surveys the XAI landscape along four dimensions: the major families of explanation techniques-intrinsically interpretable models, post-hoc local methods such as LIME and SHAP, gradient- and attention-based visual explanations such as Grad-CAM, and counterfactual explanations; their realworld applications in domains including healthcare diagnostics, credit and financial risk scoring, and autonomous and safety-critical systems; the challenges that continue to limit adoption, including explanation fidelity, the absence of standardised evaluation metrics, computational cost, and scalability to large generative models; and the future directions the field must pursue. Drawing on a structured review of the recent literature, we compare techniques along scope, fidelity, and cost, and examine the regulatory pressures, including the EU AI Act and the GDPR "right to explanation," that are accelerating adoption. The synthesis shows that no single XAI technique is universally superior; effective explainability requires matching the method to the model class, the application domain, and the stakes of the decision. We conclude that explainability is a necessary, though not sufficient, condition for trustworthy Al, and outline concrete directions for future research, including standardised benchmarks, human-centred evaluation, and explainability for large generative models. Keywords: Explainable Al; interpretability; XAI applications; model transparency; trustworthy Al