  Interpretability techniques aim to provide the rationale behind a model's
decision, typically by explaining either an individual prediction (local
explanation, e.g. 'why is this patient diagnosed with this condition') or a
class of predictions (global explanation, e.g. 'why is this set of patients
diagnosed with this condition in general'). While there are many methods
focused on either one, few frameworks can provide both local and global
explanations in a consistent manner. In this work, we combine two powerful
existing techniques, one local (Integrated Gradients, IG) and one global
(Testing with Concept Activation Vectors), to provide local and global
concept-based explanations. We first sanity check our idea using two synthetic
datasets with a known ground truth, and further demonstrate with a benchmark
natural image dataset. We test our method with various concepts, target
classes, model architectures and IG parameters (e.g. baselines). We show that
our method improves global explanations over vanilla TCAV when compared to
ground truth, and provides useful local insights. Finally, a user study
demonstrates the usefulness of the method compared to no or global explanations
only. We hope our work provides a step towards building bridges between many
existing local and global methods to get the best of both worlds.
