As a reflection of the social and ethical concerns related to the increasing use of AI-based systems in modern society, the field of explainable AI (XAI) has gained tremendous momentum in recent years. XAI mainly consists of two complementary avenues of research that aim at shedding some light into the inner-workings of complex ML models. On the one hand, post-hoc explanation methods apply to existing models that have often been trained with the sole purpose of accomplishing a given task as efficiently as possible (e.g., accuracy in a classification task). On the other hand, self-explainable models are designed and trained to produce their own explanations along with their decision. The appeal of selfexplainable models resides in the fact that rather than using an approximation (i.e., a post-hoc explanation method) to understand a complex model, it is better to directly enforce a simpler (and more understandable) decision-making process during the design and training of the ML model, provided that such a model would exhibit an acceptable level of performance.

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.

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 [42] (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.

In this work, we perform an in-depth analysis of the visualisation methods implemented in two popular self-explaining models for visual classification based on prototypes - ProtoPNet and ProtoTree. Using two fine-grained datasets (CUB-200-2011 and Stanford Cars), we first show that such methods do not correctly identify the regions of interest inside of the images, and therefore do not reflect the model behaviour. Secondly, using a deletion metric, we demonstrate quantitatively that saliency methods such as Smoothgrads or PRP provide more faithful image patches. We also propose a new relevance metric based on the segmentation of the object provided in some datasets (e.g. CUB-200-2011) and show that the imprecise patch visualisations generated by ProtoPNet and ProtoTree can create a false sense of bias that can be mitigated by the use of more faithful methods. Finally, we discuss the implications of our findings for other prototype-based models sharing the same visualisation method.

Explaining black-box Artificial Intelligence (AI) models is a cornerstone for trustworthy AI and a prerequisite for its use in safety critical applications such that AI models can reliably assist humans in critical decisions. However, instead of trying to explain our models post-hoc, we need models which are interpretable-by-design built on a reasoning process similar to humans that exploits meaningful high-level concepts such as shapes, texture or object parts. Learning such concepts is often hindered by its need for explicit specification and annotation up front. Instead, prototype-based learning approaches such as ProtoPNet claim to discover visually meaningful prototypes in an unsupervised way. In this work, we propose a set of properties that those prototypes have to fulfill to enable human analysis, e.g. as part of a reliable model assessment case, and analyse such existing methods in the light of these properties. Given a 'Guess who?' game, we find that these prototypes still have a long way ahead towards definite explanations. We quantitatively validate our findings by conducting a user study indicating that many of the learnt prototypes are not considered useful towards human understanding. We discuss about the missing links in the existing methods and present a potential real-world application motivating the need to progress towards truly human-interpretable prototypes.

We introduce ProtoPool, an interpretable image classification model with a pool of prototypes shared by the classes. The training is more straightforward than in the existing methods because it does not require the pruning stage. It is obtained by introducing a fully differentiable assignment of prototypes to particular classes. Moreover, we introduce a novel focal similarity function to focus the model on the rare foreground features. We show that ProtoPool obtains state-of-the-art accuracy on the CUB-200-2011 and the Stanford Cars datasets, substantially reducing the number of prototypes. We provide a theoretical analysis of the method and a user study to show that our prototypes are more distinctive than those obtained with competitive methods.

One of the biggest obstacles in the adoption of Artificial Intelligence is that it cannot explain what a prediction is based on. These machine-learning systems are so-called black boxes when the reasoning for a decision is not self-evident to a user. Meike Nauta, Ph.D. candidate at the Data Science group within the EEMCS faculty of the University of Twente, created a model to address the black-box nature of deep learning models. Algorithms can already make accurate predictions, such as medical diagnoses, but they cannot explain how they arrived at such a prediction. In recent years, a lot of attention has been paid to the explainable AI field.

Interpretable machine learning addresses the black-box nature of deep neural networks. Visual prototypes have been suggested for intrinsically interpretable image recognition, instead of generating post-hoc explanations that approximate a trained model. However, a large number of prototypes can be overwhelming. To reduce explanation size and improve interpretability, we propose the Neural Prototype Tree (ProtoTree), a deep learning method that includes prototypes in an interpretable decision tree to faithfully visualize the entire model. In addition to global interpretability, a path in the tree explains a single prediction. Each node in our binary tree contains a trainable prototypical part. The presence or absence of this prototype in an image determines the routing through a node. Decision making is therefore similar to human reasoning: Does the bird have a red throat? And an elongated beak? Then it's a hummingbird! We tune the accuracy-interpretability trade-off using ensembling and pruning. We apply pruning without sacrificing accuracy, resulting in a small tree with only 8 prototypes along a path to classify a bird from 200 species. An ensemble of 5 ProtoTrees achieves competitive accuracy on the CUB-200-2011 and Stanford Cars data sets. Code is available at https://github.com/M-Nauta/ProtoTree