  Current machine learning models have shown high efficiency in solving a wide
variety of real-world problems. However, their black box character poses a
major challenge for the understanding and traceability of the underlying
decision-making strategies. As a remedy, many post-hoc explanation and
self-explanatory methods have been developed to interpret the models' behavior.
These methods, in addition, enable the identification of artifacts that can be
learned by the model as class-relevant features. In this work, we provide a
detailed case study of the self-explaining network, ProtoPNet, in the presence
of a spectrum of artifacts. Accordingly, we identify the main drawbacks of
ProtoPNet, especially, its coarse and spatially imprecise explanations. We
address these limitations by introducing Prototypical Relevance Propagation
(PRP), a novel method for generating more precise model-aware explanations.
Furthermore, in order to obtain a clean dataset, we propose to use multi-view
clustering strategies for segregating the artifact images using the PRP
explanations, thereby suppressing the potential artifact learning in the
models.
