  Interpretable part-prototype models are computer vision models that are
explainable by design. The models learn prototypical parts and recognise these
components in an image, thereby combining classification and explanation.
Despite the recent attention for intrinsically interpretable models, there is
no comprehensive overview on evaluating the explanation quality of
interpretable part-prototype models. Based on the Co-12 properties for
explanation quality as introduced in arXiv:2201.08164 (e.g., correctness,
completeness, compactness), we review existing work that evaluates
part-prototype models, reveal research gaps and outline future approaches for
evaluation of the explanation quality of part-prototype models. This paper,
therefore, contributes to the progression and maturity of this relatively new
research field on interpretable part-prototype models. We additionally provide
a ``Co-12 cheat sheet'' that acts as a concise summary of our findings on
evaluating part-prototype models.
