  Recent papers in explainable AI have made a compelling case for
counterfactual modes of explanation. While counterfactual explanations appear
to be extremely effective in some instances, they are formally equivalent to
adversarial examples. This presents an apparent paradox for explainability
researchers: if these two procedures are formally equivalent, what accounts for
the explanatory divide apparent between counterfactual explanations and
adversarial examples? We resolve this paradox by placing emphasis back on the
semantics of counterfactual expressions. Producing satisfactory explanations
for deep learning systems will require that we find ways to interpret the
semantics of hidden layer representations in deep neural networks.
