  Explaining the output of a complicated machine learning model like a deep
neural network (DNN) is a central challenge in machine learning. Several
proposed local explanation methods address this issue by identifying what
dimensions of a single input are most responsible for a DNN's output. The goal
of this work is to assess the sensitivity of local explanations to DNN
parameter values. Somewhat surprisingly, we find that DNNs with
randomly-initialized weights produce explanations that are both visually and
quantitatively similar to those produced by DNNs with learned weights. Our
conjecture is that this phenomenon occurs because these explanations are
dominated by the lower level features of a DNN, and that a DNN's architecture
provides a strong prior which significantly affects the representations learned
at these lower layers. NOTE: This work is now subsumed by our recent
manuscript, Sanity Checks for Saliency Maps (to appear NIPS 2018), where we
expand on findings and address concerns raised in Sundararajan et. al. (2018).
