  There is a growing concern that the recent progress made in AI, especially
regarding the predictive competence of deep learning models, will be undermined
by a failure to properly explain their operation and outputs. In response to
this disquiet counterfactual explanations have become massively popular in
eXplainable AI (XAI) due to their proposed computational psychological, and
legal benefits. In contrast however, semifactuals, which are a similar way
humans commonly explain their reasoning, have surprisingly received no
attention. Most counterfactual methods address tabular rather than image data,
partly due to the nondiscrete nature of the latter making good counterfactuals
difficult to define. Additionally generating plausible looking explanations
which lie on the data manifold is another issue which hampers progress. This
paper advances a novel method for generating plausible counterfactuals (and
semifactuals) for black box CNN classifiers doing computer vision. The present
method, called PlausIble Exceptionality-based Contrastive Explanations (PIECE),
modifies all exceptional features in a test image to be normal from the
perspective of the counterfactual class (hence concretely defining a
counterfactual). Two controlled experiments compare this method to others in
the literature, showing that PIECE not only generates the most plausible
counterfactuals on several measures, but also the best semifactuals.
