The summer school for explainable AI deals with interpretability and explainability of AI and machine learning models.

XAISS aims to cover some of the most important topics in explainable AI (e.g. post-hoc interpretability in ML, interpretable representation learning in language and vision tasks, counterfactuals, human-centric explainability and others) and their applications in important subject areas (e.g. language, vision, search and recommendation systems). This summer school will involve both lectures and hands-on activities. The lectures and tutorials will be taught by leading international experts in the area of explainable AI from both academia and the industry. All the materials, content and code will be accessible to all participants with focus being placed on reducing the entry barrier to Explainable AI topics. The school will also cover a wide variety of talks that will explore societal implications of explainable AI on society for a more nuanced and holistic treatment of the topic.