  Interpretability methods for image classification assess model
trustworthiness by attempting to expose whether the model is systematically
biased or attending to the same cues as a human would. Saliency methods for
feature attribution dominate the interpretability literature, but these methods
do not address semantic concepts such as the textures, colors, or genders of
objects within an image. Our proposed Robust Concept Activation Vectors (RCAV)
quantifies the effects of semantic concepts on individual model predictions and
on model behavior as a whole. RCAV calculates a concept gradient and takes a
gradient ascent step to assess model sensitivity to the given concept. By
generalizing previous work on concept activation vectors to account for model
non-linearity, and by introducing stricter hypothesis testing, we show that
RCAV yields interpretations which are both more accurate at the image level and
robust at the dataset level. RCAV, like saliency methods, supports the
interpretation of individual predictions. To evaluate the practical use of
interpretability methods as debugging tools, and the scientific use of
interpretability methods for identifying inductive biases (e.g. texture over
shape), we construct two datasets and accompanying metrics for realistic
benchmarking of semantic interpretability methods. Our benchmarks expose the
importance of counterfactual augmentation and negative controls for quantifying
the practical usability of interpretability methods.
