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Predictive models are omnipresent in automated and assisted decision makingscenarios. But for the most part they are used as black boxes which output aprediction without understanding partially or even completely how differentfeatures influence the model prediction avoiding algorithmic transparency.Rankings are ordering over items encoding implicit comparisons typicallylearned using a family of features using learning-to-rank models. In this paperwe focus on how best we can understand the decisions made by a ranker in apost-hoc model agnostic manner. We operate on the notion of interpretabilitybased on explainability of rankings over an interpretable feature space.Furthermore we train a tree based model (inherently interpretable) using labelsfrom the ranker, called secondary training data to provide explanations.Consequently, we attempt to study how well does a subset of features,potentially interpretable, explain the full model under different trainingsizes and algorithms. We do experiments on the learning to rank datasets with30k queries and report results that serve show in certain settings we can learna faithful interpretable ranker.

Posthoc Interpretability of Learning to Rank Models using Secondary Training Data