Global Explanations of Neural Networks: Mapping the Landscape of Predictions

Understand

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.