Explainable AI (XAI) – Enhancing Interpretability of Deep Learning Models for Critical Applications

Abstract

Deep learning models have achieved remarkable performance across critical applications including healthcare, finance, and autonomous systems. However, their black-box nature poses significant challenges for deployment in high-stakes domains where transparency and accountability are paramount. This paper presents a comprehensive technical framework for enhancing interpretability of deep learning models through explainable artificial intelligence (XAI) methodologies. We evaluate multiple XAI techniques including SHAP, LIME, Grad-CAM, and layerwise relevance propagation across diverse datasets from healthcare and financial domains. Our approach demonstrates significant improvements in model interpretability while maintaining predictive accuracy, achieving faithfulness scores of 0.87±0.05 and stability metrics exceeding 0.82 across tested applications. The proposed methodology addresses critical requirements for regulatory compliance and trustworthy AI deployment in mission-critical systems. Results indicate that post-hoc explanation methods combined with rigorous evaluation frameworks provide viable pathways for transparent AI implementation in critical applications.