  Ensuring the quality of black-box Deep Neural Networks (DNNs) has become ever
more significant, especially in safety-critical domains such as automated
driving. While global concept encodings generally enable a user to test a model
for a specific concept, linking global concept encodings to the local
processing of single network inputs reveals their strengths and limitations.
Our proposed framework global-to-local Concept Attribution (glCA) uses
approaches from local (why a specific prediction originates) and global (how a
model works generally) eXplainable Artificial Intelligence (xAI) to test DNNs
for a predefined semantical concept locally. The approach allows for
conditioning local, post-hoc explanations on predefined semantic concepts
encoded as linear directions in the model's latent space. Pixel-exact scoring
concerning the global concept usage assists the tester in further understanding
the model processing of single data points for the selected concept. Our
approach has the advantage of fully covering the model-internal encoding of the
semantic concept and allowing the localization of relevant concept-related
information. The results show major differences in the local perception and
usage of individual global concept encodings and demand for further
investigations regarding obtaining thorough semantic concept encodings.
