  Semantic Embeddings are a popular way to represent knowledge in the field of
zero-shot learning. We observe their interpretability and discuss their
potential utility in a safety-critical context. Concretely, we propose to use
them to add introspection and error detection capabilities to neural network
classifiers. First, we show how to create embeddings from symbolic domain
knowledge. We discuss how to use them for interpreting mispredictions and
propose a simple error detection scheme. We then introduce the concept of
semantic distance: a real-valued score that measures confidence in the semantic
space. We evaluate this score on a traffic sign classifier and find that it
achieves near state-of-the-art performance, while being significantly faster to
compute than other confidence scores. Our approach requires no changes to the
original network and is thus applicable to any task for which domain knowledge
is available.
