Enhancing Explainability in Deep Learning: A Graph-Based Approach to Interpretable AI
Abstract
Despite the remarkable success of deep learning models, their black-box nature remains a challenge in high-stakes applications such as healthcare and finance. This paper introduces a graph-based approach to improve interpretability in deep neural networks. By leveraging graph neural networks (GNNs) and attention mechanisms, we develop a method that visualizes decision-making pathways in complex models. Experimental results demonstrate how our approach enhances transparency without sacrificing performance. We also discuss the ethical implications of explainable AI and propose guidelines for integrating interpretability into AI model design.
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