Researchers have developed a new approach to creating 'counterfactual' images that explain how image classifiers make predictions. This is done by identifying the factors that are unique to each dataset and swapping them between two datasets with different classes, such as healthy and patients. The method, called Counterfactual Contrastive Analysis, provides model-agnostic explanations that are less sensitive to classifier biases. It uses StyleGAN2 to generate high-quality images and has been tested on three medical imaging datasets. --- Why it matters: This matters because it allows for more robust and reliable explanations of image classifiers' predictions, which is essential in applications such as medical diagnosis where accurate understanding of model decisions is critical. Source: https://arxiv.org/abs/2608.19032