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We introduce a method, KL-LIME, for explaining predictions of Bayesianpredictive models by projecting the information in the predictive distributionlocally to a simpler, interpretable explanation model. The proposed approachcombines the recent Local Interpretable Model-agnostic Explanations (LIME)method with ideas from Bayesian projection predictive variable selectionmethods. The information theoretic basis helps in navigating the trade-offbetween explanation fidelity and complexity. We demonstrate the method inexplaining MNIST digit classifications made by a Bayesian deep convolutionalneural network.

Local Interpretable Model-agnostic Explanations of Bayesian Predictive Models via Kullback-Leibler Projections