  It is well-known that real-world changes constituting distribution shift
adversely affect model performance. How to characterize those changes in an
interpretable manner is poorly understood. Existing techniques to address this
problem take the form of shift explanations that elucidate how to map samples
from the original distribution toward the shifted one by reducing the disparity
between these two distributions. However, these methods can introduce group
irregularities, leading to explanations that are less feasible and robust. To
address these issues, we propose Group-aware Shift Explanations (GSE), a method
that produces interpretable explanations by leveraging worst-group optimization
to rectify group irregularities. We demonstrate how GSE not only maintains
group structures, such as demographic and hierarchical subpopulations, but also
enhances feasibility and robustness in the resulting explanations in a wide
range of tabular, language, and image settings.

Machine Learning