XAI4science: Knowledge Discovery and Trust through Interpretable Foundation Models

Climate change represents an existential race that humanity cannot afford to lose. Through accurate weather forecasting models and Environmental Sciences, researchers make informed choices towards a more sustainable future. While traditional models require immense computational resources, as more data is collected, Machine Learning (ML) has proven to be a viable and efficient solution for it. Weather and Climate forecasting Foundation Models (FM) have shown that comparable results can be achieved with a fraction of the resources, opening the field to smaller countries and research teams with less investment and representation. While this provided a solution to the “efficiency” crisis in climate modeling, it introduced a “transparency” crisis. In fact, these black-box models pose a significant threat to scientific trust and social equity, impeding wider adoptions, such as in extreme weather event preparation and intervention, or towards sustainable initiatives and global net-zero projects. This workshop focuses on two converging crises: the technical challenge of extracting physically consistent explanations from high-dimensional FM and the risk that non-transparent models will reinforce existing inequalities through biased resource allocation. Therefore, by integrating eXplainable Artificial Intelligence (XAI) in the modeling pipeline, we aim at reducing the interpretability gap and bias of FMs. By bringing together ML researchers, climate scientists, and policy experts, we aim to investigate the fundamental roles of interpretable architectures in the ongoing challenge of extreme weather events and towards a low-carbon society for a better future.