Explanation by Progressive Exaggeration
This addresses the need for interpretability in high-stakes applications like medical imaging, though it is an incremental improvement over existing explanation methods.
The paper tackles the problem of explaining black-box classifier decisions by generating progressive counterfactual variations that gradually change the predicted class, preserving unrelated features to allow users to traverse the decision boundary.
As machine learning methods see greater adoption and implementation in high stakes applications such as medical image diagnosis, the need for model interpretability and explanation has become more critical. Classical approaches that assess feature importance (e.g. saliency maps) do not explain how and why a particular region of an image is relevant to the prediction. We propose a method that explains the outcome of a classification black-box by gradually exaggerating the semantic effect of a given class. Given a query input to a classifier, our method produces a progressive set of plausible variations of that query, which gradually changes the posterior probability from its original class to its negation. These counter-factually generated samples preserve features unrelated to the classification decision, such that a user can employ our method as a "tuning knob" to traverse a data manifold while crossing the decision boundary. Our method is model agnostic and only requires the output value and gradient of the predictor with respect to its input.