Understanding the (Extra-)Ordinary: Validating Deep Model Decisions with Prototypical Concept-based Explanations

Maximilian Dreyer, Reduan Achtibat, Wojciech Samek, Sebastian Lapuschkin; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 3491-3501

Ensuring both transparency and safety is critical when deploying Deep Neural Networks (DNNs) in high-risk applications such as medicine. The field of explainable AI (XAI) has proposed various methods to comprehend the decision-making processes of opaque DNNs. However only few XAI methods are suitable of ensuring safety in practice as they heavily rely on repeated labor-intensive and possibly biased human assessment. In this work we present a novel post-hoc concept-based XAI framework that conveys besides instance-wise (local) also class-wise (global) decision-making strategies via prototypes. What sets our approach apart is the combination of local and global strategies enabling a clearer understanding of the (dis-)similarities in model decisions compared to the expected (prototypical) concept use ultimately reducing the dependence on human long-term assessment. Quantifying the deviation from prototypical behavior not only allows to associate predictions with specific model sub-strategies but also to detect outlier behavior. As such our approach constitutes an intuitive and explainable tool for model validation. We demonstrate the effectiveness of our approach in identifying out-of-distribution samples spurious model behavior and data quality issues across three datasets (ImageNet CUB-200 and CIFAR-10) utilizing VGG ResNet and EfficientNet architectures. Code is available at https://github.com/maxdreyer/pcx.