  Predictions are the currency of a machine learning model, and to understand
the model's behavior over segments of a dataset, or over time, is an important
problem in machine learning research and practice. There currently is no
systematic framework to understand this drift in prediction distributions over
time or between two semantically meaningful slices of data, in terms of the
input features and points. We propose GroupShapley and GroupIG (Integrated
Gradients), as axiomatically justified methods to tackle this problem. In doing
so, we re-frame all current feature/data importance measures based on the
Shapley value as essentially problems of distributional comparisons, and unify
them under a common umbrella. We axiomatize certain desirable properties of
distributional difference, and study the implications of choosing them
empirically.
