Wasserstein Distances Made Explainable: Insights Into Dataset Shifts and Transport Phenomena

Researchers have developed a new Explainable AI method that makes Wasserstein distances more interpretable by attributing distance calculations to specific data components like subgroups and features. The framework enables better analysis of dataset shifts and transport phenomena across diverse applications with high accuracy.