  Explaining deep learning model inferences is a promising venue for scientific
understanding, improving safety, uncovering hidden biases, evaluating fairness,
and beyond, as argued by many scholars. One of the principal benefits of
counterfactual explanations is allowing users to explore "what-if" scenarios
through what does not and cannot exist in the data, a quality that many other
forms of explanation such as heatmaps and influence functions are inherently
incapable of doing. However, most previous work on generative explainability
cannot disentangle important concepts effectively, produces unrealistic
examples, or fails to retain relevant information. We propose a novel approach,
DISSECT, that jointly trains a generator, a discriminator, and a concept
disentangler to overcome such challenges using little supervision. DISSECT
generates Concept Traversals (CTs), defined as a sequence of generated examples
with increasing degrees of concepts that influence a classifier's decision. By
training a generative model from a classifier's signal, DISSECT offers a way to
discover a classifier's inherent "notion" of distinct concepts automatically
rather than rely on user-predefined concepts. We show that DISSECT produces CTs
that (1) disentangle several concepts, (2) are influential to a classifier's
decision and are coupled to its reasoning due to joint training (3), are
realistic, (4) preserve relevant information, and (5) are stable across similar
inputs. We validate DISSECT on several challenging synthetic and realistic
datasets where previous methods fall short of satisfying desirable criteria for
interpretability and show that it performs consistently well and better than
existing methods. Finally, we present experiments showing applications of
DISSECT for detecting potential biases of a classifier and identifying spurious
artifacts that impact predictions.
