  Recent work has suggested post-hoc explainers might be ineffective for
detecting spurious correlations in Deep Neural Networks (DNNs). However, we
show there are serious weaknesses with the existing evaluation frameworks for
this setting. Previously proposed metrics are extremely difficult to interpret
and are not directly comparable between explainer methods. To alleviate these
constraints, we propose a new evaluation methodology, Explainer Divergence
Scores (EDS), grounded in an information theory approach to evaluate
explainers. EDS is easy to interpret and naturally comparable across
explainers. We use our methodology to compare the detection performance of
three different explainers - feature attribution methods, influential examples
and concept extraction, on two different image datasets. We discover post-hoc
explainers often contain substantial information about a DNN's dependence on
spurious artifacts, but in ways often imperceptible to human users. This
suggests the need for new techniques that can use this information to better
detect a DNN's reliance on spurious correlations.
