Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives
Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Paishun Ting,, Karthikeyan Shanmugam, Payel Das

TL;DR
This paper introduces a novel contrastive explanation method for neural network classifications that highlights both necessary present and absent features, improving interpretability for human experts across diverse datasets.
Contribution
The method explicitly incorporates the role of absent features in explanations, a novel aspect not addressed by prior neural network explanation techniques.
Findings
Effective in explaining classifications in MNIST, fraud detection, and brain activity datasets.
Produces explanations that are precise and easily understandable by humans.
Demonstrates the importance of considering absent features in model interpretability.
Abstract
In this paper we propose a novel method that provides contrastive explanations justifying the classification of an input by a black box classifier such as a deep neural network. Given an input we find what should be %necessarily and minimally and sufficiently present (viz. important object pixels in an image) to justify its classification and analogously what should be minimally and necessarily \emph{absent} (viz. certain background pixels). We argue that such explanations are natural for humans and are used commonly in domains such as health care and criminology. What is minimally but critically \emph{absent} is an important part of an explanation, which to the best of our knowledge, has not been explicitly identified by current explanation methods that explain predictions of neural networks. We validate our approach on three real datasets obtained from diverse domains; namely, a…
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Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Adversarial Robustness in Machine Learning · Anomaly Detection Techniques and Applications
