  The notion of concept drift refers to the phenomenon that the distribution,
which is underlying the observed data, changes over time; as a consequence
machine learning models may become inaccurate and need adjustment. While there
do exist methods to detect concept drift or to adjust models in the presence of
observed drift, the question of explaining drift has hardly been considered so
far. This problem is of importance, since it enables an inspection of the most
prominent features where drift manifests itself; hence it enables human
understanding of the necessity of change and it increases acceptance of
life-long learning models. In this paper we present a novel technology, which
characterizes concept drift in terms of the characteristic change of spatial
features represented by typical examples based on counterfactual explanations.
We establish a formal definition of this problem, derive an efficient
algorithmic solution based on counterfactual explanations, and demonstrate its
usefulness in several examples.
