  For an explanation of a deep learning model to be effective, it must provide
both insight into a model and suggest a corresponding action in order to
achieve some objective. Too often, the litany of proposed explainable deep
learning methods stop at the first step, providing practitioners with insight
into a model, but no way to act on it. In this paper, we propose contextual
decomposition explanation penalization (CDEP), a method which enables
practitioners to leverage existing explanation methods in order to increase the
predictive accuracy of deep learning models. In particular, when shown that a
model has incorrectly assigned importance to some features, CDEP enables
practitioners to correct these errors by directly regularizing the provided
explanations. Using explanations provided by contextual decomposition (CD)
(Murdoch et al., 2018), we demonstrate the ability of our method to increase
performance on an array of toy and real datasets.
