  Many decision making systems deployed in the real world are not static - a
phenomenon known as model adaptation takes place over time. The need for
transparency and interpretability of AI-based decision models is widely
accepted and thus have been worked on extensively. Usually, explanation methods
assume a static system that has to be explained. Explaining non-static systems
is still an open research question, which poses the challenge how to explain
model adaptations. In this contribution, we propose and (empirically) evaluate
a framework for explaining model adaptations by contrastive explanations. We
also propose a method for automatically finding regions in data space that are
affected by a given model adaptation and thus should be explained.
