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.

翻译：部分原型网络近年来作为当前众多黑盒图像分类器的可解释替代方法而受到关注。然而，从人类用户视角来看，这些方法的可解释性尚未得到充分探索。本研究设计了一个面向部分原型模型可解释性的人类视角评估框架。该框架包含三个可操作指标及配套实验。为验证框架的有效性，我们利用亚马逊土耳其机器人开展了大量实验。这些实验不仅证明了我们的框架具备评估不同部分原型模型可解释性的能力，而且据我们所知，这是首个在统一框架下对此类方法进行的最全面的评估工作。