  The ability of to explain neural network decisions goes hand in hand with
their safe deployment. Several methods have been proposed to highlight features
important for a given network decision. However, there is no consensus on how
to measure effectiveness of these methods. We propose a new procedure for
evaluating explanations. We use it to investigate visual explanations extracted
from a range of possible sources in a neural network. We quantify the benefit
of combining these sources and challenge a recent appeal for taking bias
parameters into account. We support our conclusions with a general assessment
of the impact of bias parameters in ImageNet classifiers
