  A barrier to the wider adoption of neural networks is their lack of
interpretability. While local explanation methods exist for one prediction,
most global attributions still reduce neural network decisions to a single set
of features. In response, we present an approach for generating global
attributions called GAM, which explains the landscape of neural network
predictions across subpopulations. GAM augments global explanations with the
proportion of samples that each attribution best explains and specifies which
samples are described by each attribution. Global explanations also have
tunable granularity to detect more or fewer subpopulations. We demonstrate that
GAM's global explanations 1) yield the known feature importances of simulated
data, 2) match feature weights of interpretable statistical models on real
data, and 3) are intuitive to practitioners through user studies. With more
transparent predictions, GAM can help ensure neural network decisions are
generated for the right reasons.

Machine Learning;Artificial Intelligence;Machine Learning