  Part-prototype Networks (ProtoPNets) are concept-based classifiers designed
to achieve the same performance as black-box models without compromising
transparency. ProtoPNets compute predictions based on similarity to
class-specific part-prototypes learned to recognize parts of training examples,
making it easy to faithfully determine what examples are responsible for any
target prediction and why. However, like other models, they are prone to
picking up confounders and shortcuts from the data, thus suffering from
compromised prediction accuracy and limited generalization. We propose
ProtoPDebug, an effective concept-level debugger for ProtoPNets in which a
human supervisor, guided by the model's explanations, supplies feedback in the
form of what part-prototypes must be forgotten or kept, and the model is
fine-tuned to align with this supervision. Our experimental evaluation shows
that ProtoPDebug outperforms state-of-the-art debuggers for a fraction of the
annotation cost. An online experiment with laypeople confirms the simplicity of
the feedback requested to the users and the effectiveness of the collected
feedback for learning confounder-free part-prototypes. ProtoPDebug is a
promising tool for trustworthy interactive learning in critical applications,
as suggested by a preliminary evaluation on a medical decision making task.
