Clarity: an improved gradient method for producing quality visual counterfactual explanations
This work addresses the need for better interpretability in AI systems for users like researchers and practitioners, though it appears incremental as it builds on existing gradient-based methods.
The authors tackled the problem of generating realistic visual counterfactual explanations for image classifiers by improving gradient quality using generative models and classifier ensembles, resulting in a novel model called Clarity that produces competitive, realistic explanations across all images.
Visual counterfactual explanations identify modifications to an image that would change the prediction of a classifier. We propose a set of techniques based on generative models (VAE) and a classifier ensemble directly trained in the latent space, which all together, improve the quality of the gradient required to compute visual counterfactuals. These improvements lead to a novel classification model, Clarity, which produces realistic counterfactual explanations over all images. We also present several experiments that give insights on why these techniques lead to better quality results than those in the literature. The explanations produced are competitive with the state-of-the-art and emphasize the importance of selecting a meaningful input space for training.