  As input data distributions evolve, the predictive performance of machine
learning models tends to deteriorate. In the past, predictive performance was
considered the key indicator to monitor. However, explanation aspects have come
to attention within the last years. In this work, we investigate how model
predictive performance and model explanation characteristics are affected under
distribution shifts and how these key indicators are related to each other for
tabular data. We find that the modeling of explanation shifts can be a better
indicator for the detection of predictive performance changes than
state-of-the-art techniques based on representations of distribution shifts. We
provide a mathematical analysis of different types of distribution shifts as
well as synthetic experimental examples.
