Winter School on Causality and Explainable AI

While often studied separately, these fields are complementary: causality uncovers the underlying cause-effect mechanisms of data, while explainability sheds light on the behavior of predictive models. By bringing them together, this will open up new avenues for enhancing the reliability and interpretability of AI models. Designed for Master’s and PhD students, the program combines lectures, tutorials, and interdisciplinary discussions, including an accessible introductory tutorial tailored to Master’s students.