  We investigate whether three types of post hoc model explanations--feature
attribution, concept activation, and training point ranking--are effective for
detecting a model's reliance on spurious signals in the training data.
Specifically, we consider the scenario where the spurious signal to be detected
is unknown, at test-time, to the user of the explanation method. We design an
empirical methodology that uses semi-synthetic datasets along with
pre-specified spurious artifacts to obtain models that verifiably rely on these
spurious training signals. We then provide a suite of metrics that assess an
explanation method's reliability for spurious signal detection under various
conditions. We find that the post hoc explanation methods tested are
ineffective when the spurious artifact is unknown at test-time especially for
non-visible artifacts like a background blur. Further, we find that feature
attribution methods are susceptible to erroneously indicating dependence on
spurious signals even when the model being explained does not rely on spurious
artifacts. This finding casts doubt on the utility of these approaches, in the
hands of a practitioner, for detecting a model's reliance on spurious signals.
