Transparent Decision-Making with Explainable Ai (Xai): Advances in Interpretable Deep Learning.

Abstract

As artificial intelligence (AI) systems, particularly deep learning models, become increasingly integrated into critical decision-making processes, the demand for transparency and interpretability grows. Explainable AI (XAI) addresses the "black-box" nature of deep learning by developing methods that make AI decisions understandable to humans. This paper explores recent advances in interpretable deep learning models, focusing on techniques such as attention mechanisms, SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and self-explaining neural networks. We evaluate their effectiveness in enhancing transparency across healthcare, finance, and autonomous systems. Finally, we discuss challenges and future directions for deploying XAI in real-world applications while maintaining model accuracy and trustworthiness.