  We show how fitting sparse linear models over learned deep feature
representations can lead to more debuggable neural networks. These networks
remain highly accurate while also being more amenable to human interpretation,
as we demonstrate quantiatively via numerical and human experiments. We further
illustrate how the resulting sparse explanations can help to identify spurious
correlations, explain misclassifications, and diagnose model biases in vision
and language tasks. The code for our toolkit can be found at
https://github.com/madrylab/debuggabledeepnetworks.

Machine Learning;Machine Learning