  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 handwritten digits
dataset MNIST, a large procurement fraud dataset and a brain activity strength
dataset. In all three cases, we witness the power of our approach in generating
precise explanations that are also easy for human experts to understand and
evaluate.
