Evaluations and Methods for Explanation through Robustness Analysis

Understand

Feature based explanations, that provide importance of each feature towards the model prediction, is arguably one of the most intuitive ways to explain a model.

• In this paper, we establish a novel set of evaluation criteria for such feature based explanations by robustness analysis.
• In contrast to existing evaluations which require us to specify some way to "remove" features that could inevitably introduces biases and artifacts, we make use of the subtler notion of smaller adversarial perturbations.
• By optimizing towards our proposed evaluation criteria, we obtain new explanations that are loosely necessary and sufficient for a prediction.