Explainable Deep

Explainable Deep Learning (XDL) aims to make the decision-making processes of deep learning models more transparent and understandable, addressing the "black box" nature of these powerful but opaque systems. Current research focuses on developing methods to provide explanations for deep learning predictions, often incorporating techniques like attention mechanisms, surrogate models, and rule-based systems within architectures such as convolutional neural networks and recurrent neural networks. This field is crucial for building trust and facilitating the adoption of deep learning in high-stakes applications like medical diagnosis and financial modeling, where understanding the reasoning behind predictions is paramount.