A Comprehensive Review of Explainable AI (XAI) Methods in Deep Learning

Abstract

The proliferation of deep learning models across critical domains has been met with a growing demand for transparency and accountability. The inherent complexity of these models, often characterized as "black boxes," poses significant challenges to their adoption in high-stakes environments such as healthcare and finance, where understanding the rationale behind a decision is as crucial as the decision itself. This review provides a comprehensive and systematic survey of the field of Explainable AI (XAI) as it pertains to deep learning. It begins by establishing the imperative for explainability, driven by technical, ethical, and regulatory pressures. A detailed taxonomy is proposed to structure the diverse landscape of XAI methods, categorizing them along the dimensions of intrinsic versus post-hoc, local versus global, and model-agnostic versus model-specific. The core of this review is a deep dive into the foundational post-hoc explanation techniques, including the local surrogate modelling of LIME, the game-theoretic framework of SHAP, and the gradient-based approaches of Grad-CAM and Integrated Gradients. Intrinsically interpretable methods, most notably the attention mechanism in Transformer models, are also analysed. Furthermore, the paper critically examines the methodologies for evaluating XAI techniques, contrasting functionally grounded metrics like fidelity and robustness with human-grounded, in-the-loop evaluations. Through case studies in healthcare and finance, the practical application and stakeholder-specific utility of these methods are illustrated. The review concludes by identifying key open challenges and charting future research directions, including the critical need for standardized evaluation benchmarks, the pursuit of causal explanations over correlational ones, and the emerging interplay between XAI and generative AI. This work aims to serve as a definitive reference for researchers and practitioners, providing a structured understanding of the principles, techniques, and future trajectory of explainability in deep learning.