Generating Contrastive Explanations with Monotonic Attribute Functions.
Ronny Luss, Pin‐Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Karthikeyan Shanmugam, Chun‐Chen Tu · arXiv (Cornell University) · 2019
Explaining decisions of deep neural networks is a hot research topic with applications in medical imaging, video surveillance, and self driving cars. Many methods have been proposed in literature to explain these decisions by identifying relevance of different pixels, limiting the types of explanations possible. In this paper, we propose a method that can generate contrastive explanations for such data where we not only highlight aspects that are in themselves sufficient to justify the classification by the deep model, but also new aspects which if added will change the classification. In order to move beyond the limitations of previous explanations, our key contribution is how we define addition for such rich data in a formal yet humanly interpretable way that leads to meaningful results. This was one of the open questions laid out in in Dhurandhar this http URL. (2018) [6], which proposed a general framework for creating (local) contrastive explanations for deep models, but is limited to simple use cases such as black/white images. We showcase the efficacy of our approach on three diverse image data sets (faces, skin lesions, and fashion apparel) in creating intuitive explanations that are also quantitatively superior compared with other state-of-the-art interpretability methods. A thorough user study with 200 individuals asks how well the various methods are understood by humans and demonstrates which aspects of contrastive explanations are most desirable.