On the Interpretability of Part-Prototype Based Classifiers: A Human
  Centric Analysis
Omid Davoodi Shayan Mohammadizadehsamakosh Majid Komeili
Part-prototype networks have recently become methods of interest as an
interpretable alternative to many of the current black-box image classifiers.
However, the interpretability of these methods from the perspective of human
users has not been sufficiently explored. In this work, we have devised a
framework for evaluating the interpretability of part-prototype-based models
from a human perspective. The proposed framework consists of three actionable
metrics and experiments. To demonstrate the usefulness of our framework, we
performed an extensive set of experiments using Amazon Mechanical Turk. They
not only show the capability of our framework in assessing the interpretability
of various part-prototype-based models, but they also are, to the best of our
knowledge, the most comprehensive work on evaluating such methods in a unified
framework.