Enhancing Interpretability in Neural Networks through Explainable AI (XAI) Techniques

The rapid advancement of neural networks in various applications, from healthcare diagnostics to financial modeling, has significantly improved the accuracy and efficiency of decision-making processes. However, these models often operate as black boxes, providing little to no insight into how they arrive at specific predictions. This lack of interpretability presents a major barrier to their adoption in critical domains where trust, accountability, and transparency are paramount. This study aims to address this issue by developing a novel framework that integrates multiple explainable AI (XAI) techniques to enhance the interpretability of neural networks. The proposed framework combines feature importance analysis, layer-wise Relevance Propagation (LRP), and visual explanation methods such as Gradient-weighted Class Activation Mapping (Grad-CAM). These techniques collectively offer a comprehensive view of the decision-making processes of neural networks, making them more transparent and understandable to stakeholders. Our experimental results demonstrate that the integrated XAI framework not only improves interpretability but also maintains high levels of accuracy, thereby bridging the gap between performance and transparency. This research provides a foundational basis for the deployment of interpretable neural networks in critical applications, ensuring that AI-driven decisions are reliable and comprehensible.