Statistical Learning and Data Science - Ludwig Maximilian University of Munich - Munich Center for Machine Learning

Machine learning models are often referred to as black boxes because their predictions are often intransparent and not easy to understand for humans. Numerous post-hoc methods from the field of interpretable machine learning have been developed in recent years to gain new insights into black box models and their underlying data. Furthermore, model interpretation helps to validate and debug models, which also contributes to a better understanding.

In the Interpretable Machine Learning & Explainable AI research group, we explore and implement approaches to improve transparency and enhance overall explainability in ML. Our research focus is mainly on but not limited to model-agnostic methods for tabular data.