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Retrieval models in information retrieval are used to rank documents fortypically under-specified queries. Today machine learning is used to learnretrieval models from click logs and/or relevance judgments that maximizes anobjective correlated with user satisfaction. As these models becomeincreasingly powerful and sophisticated, they also become harder to understand.Consequently, it is hard for to identify artifacts in training, data specificbiases and intents from a complex trained model like neural rankers even iftrained purely on text features. EXS is a search system designed specificallyto provide its users with insight into the following questions: `What is theintent of the query according to the ranker?', `Why is this document rankedhigher than another?' and `Why is this document relevant to the query?'. EXSuses a version of a popular posthoc explanation method for classifiers -- LIME,adapted specifically to answer these questions. We show how such a system caneffectively help a user understand the results of neural rankers and highlightareas of improvement.

EXS: Explainable Search Using Local Model Agnostic Interpretability