  Concept explanation is a popular approach for examining how
human-interpretable concepts impact the predictions of a model. However, most
existing methods for concept explanations are tailored to specific models. To
address this issue, this paper focuses on model-agnostic measures.
Specifically, we propose an approach to concept explanations that satisfy three
natural axioms: linearity, recursivity, and similarity. We then establish
connections with previous concept explanation methods, offering insight into
their varying semantic meanings. Experimentally, we demonstrate the utility of
the new method by applying it in different scenarios: for model selection,
optimizer selection, and model improvement using a kind of prompt editing for
zero-shot vision language models.
