  Concept-based interpretations of black-box models are often more intuitive
for humans to understand. The most widely adopted approach for concept-based
interpretation is Concept Activation Vector (CAV). CAV relies on learning a
linear relation between some latent representation of a given model and
concepts. The linear separability is usually implicitly assumed but does not
hold true in general. In this work, we started from the original intent of
concept-based interpretation and proposed Concept Gradient (CG), extending
concept-based interpretation beyond linear concept functions. We showed that
for a general (potentially non-linear) concept, we can mathematically evaluate
how a small change of concept affecting the model's prediction, which leads to
an extension of gradient-based interpretation to the concept space. We
demonstrated empirically that CG outperforms CAV in both toy examples and real
world datasets.

Machine Learning