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

Semantics and explanation: why counterfactual explanations produce adversarial examples in deep neural networks