In recent years, machine learning (ML) and artificial intelligence (AI) methods have begun to play a more and more enabling role in the sciences and in industry. In particular, the advent of large and/or complex data corpora has given rise to new technological challenges and possibilities. In his talk, Müller will first introduce explainable AI techniques and touch upon the topic of ML applications in the sciences. He will also discuss possibilities for extracting information from machine learning models to further our understanding by explaining nonlinear ML models. E.g. Machine Learning Models for Quantum Chemistry can, by applying XAI, contribute to furthering chemical understanding. Finally, Müller will briefly outline perspectives and limitations.