Explainable Deep Learning

Explainable deep learning (XDL) aims to make the decision-making processes of deep neural networks more transparent and understandable, addressing the "black box" problem that hinders trust and adoption. Current research focuses on developing methods to provide explanations at various levels (e.g., word, sentence, image regions) using techniques like layer-wise relevance propagation, SHAP values, and Grad-CAM, often applied within architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and neural ordinary differential equations (NODEs). The resulting insights enhance the reliability and usability of deep learning models across diverse fields, including medical diagnosis, financial risk assessment, and scientific discovery, by providing human-interpretable justifications for predictions.