  Deep neural networks have produced significant progress among machine
learning models in terms of accuracy and functionality, but their inner
workings are still largely unknown. Attribution methods seek to shine a light
on these "black box" models by indicating how much each input contributed to a
model's outputs. The Integrated Gradients (IG) method is a state of the art
baseline attribution method in the axiomatic vein, meaning it is designed to
conform to particular principles of attributions. We present four axiomatic
characterizations of IG, establishing IG as the unique method to satisfy
different sets of axioms among a class of attribution methods.
