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.

该研究提出了一种名为 GAM 的方法，可以生成全局归因，解释神经网络预测的全局地貌。该方法在仿真数据和真实数据上得到了验证，并且通过用户研究证明了对实践者的直观性，以提高神经网络决策的透明度。