Generative Explanation

Generative explanation aims to create understandable and insightful explanations for the decisions made by complex machine learning models, particularly "black box" models like deep neural networks and graph neural networks. Current research focuses on developing generative models, often employing autoencoders, GANs, or other generative architectures, to produce counterfactual examples or highlight salient features that drive model predictions. This work is crucial for improving the trustworthiness and interpretability of AI systems across diverse applications, ranging from neuroscience to medical image analysis, by bridging the gap between model predictions and human understanding.