Why should I trust you? Explaining the predictions of any classifier

You've trained a classifier and it's performing well on the validation set – but does the model exhibit sound judgement or is it making decisions based on spurious criteria? Can we trust the model in the real world? And can we trust a prediction (classification) it makes well enough to act on it? Can we explain why the model made the decision it did, even if the inner workings of the model are not easily understandable by humans? These are the questions that Ribeiro et al. pose in this paper, and they answer them by building LIME – an algorithm to explain the predictions of any classifier, and SP-LIME, a method for building trust in the predictions of a model overall.