Self-explainable deep neural networks are a recent class of models that can output ante-hoc local explanations that are faithful to the model's reasoning, and as such represent a step forward toward filling the gap between expressiveness and interpretability. Self-explainable graph neural networks (GNNs) aim at achieving the same in the context of graph data. This begs the question: do these models fulfill their implicit guarantees in terms of faithfulness? In this extended abstract, we analyze the faithfulness of several self-explainable GNNs using different measures of faithfulness, identify several limitations -- both in the models themselves and in the evaluation metrics -- and outline possible ways forward.

翻译：自解释深度神经网络是一类新型模型，能够输出与模型推理过程保持忠实（faithful）的前瞻性局部解释，这代表了在弥合表达性与可解释性之间差距方面的重要进展。自解释图神经网络（GNNs）旨在图数据领域实现同样的目标。这引出一个关键问题：这些模型在忠实度方面是否履行了其隐含的保证？在本扩展摘要中，我们采用多种忠实度度量方法，分析了若干自解释GNNs的忠实度表现，指出了模型本身及评估指标存在的若干局限性，并展望了可能的改进方向。