  Explaining concepts by contrasting examples is an efficient and convenient
way of giving insights into the reasons behind a classification decision. This
is of particular interest in decision-critical domains, such as medical
diagnostics. One particular challenging use case is to distinguish facial
expressions of pain and other states, such as disgust, due to high similarity
of manifestation. In this paper, we present an approach for generating
contrastive explanations to explain facial expressions of pain and disgust
shown in video sequences. We implement and compare two approaches for
contrastive explanation generation. The first approach explains a specific pain
instance in contrast to the most similar disgust instance(s) based on the
occurrence of facial expressions (attributes). The second approach takes into
account which temporal relations hold between intervals of facial expressions
within a sequence (relations). The input to our explanation generation approach
is the output of an interpretable rule-based classifier for pain and disgust.We
utilize two different similarity metrics to determine near misses and far
misses as contrasting instances. Our results show that near miss explanations
are shorter than far miss explanations, independent from the applied similarity
metric. The outcome of our evaluation indicates that pain and disgust can be
distinguished with the help of temporal relations. We currently plan
experiments to evaluate how the explanations help in teaching concepts and how
they could be enhanced by further modalities and interaction.

Artificial Intelligence;Machine Learning