  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 on
https://github.com/maxdreyer/pcx.
