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.

翻译：可解释部件原型模型是一种通过设计实现可解释性的计算机视觉模型。该类模型学习原型部件并识别图像中的这些组件，从而将分类与解释相结合。尽管近年来内在可解释模型备受关注，但目前尚未有关于可解释部件原型模型解释质量评估的全面综述。基于arXiv:2201.08164中提出的解释质量Co-12属性（如正确性、完备性、紧凑性），我们回顾了现有评估部件原型模型的研究工作，揭示了研究空白，并概述了未来评估部件原型模型解释质量的方法。因此，本文对可解释部件原型模型这一相对较新的研究领域的发展与成熟做出了贡献。此外，我们提供了一份"Co-12速查表"，作为对部件原型模型评估结果的简明总结。