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Interpretability has arisen as a key desideratum of machine learning modelsalongside performance. Approaches so far have been primarily concerned withfixed dimensional inputs emphasizing feature relevance or selection. Incontrast, we focus on temporal modeling and the problem of tailoring thepredictor, functionally, towards an interpretable family. To this end, wepropose a co-operative game between the predictor and an explainer without anya priori restrictions on the functional class of the predictor. The goal of theexplainer is to highlight, locally, how well the predictor conforms to thechosen interpretable family of temporal models. Our co-operative game is setupasymmetrically in terms of information sets for efficiency reasons. We developand illustrate the framework in the context of temporal sequence models withexamples.

Game-Theoretic Interpretability for Temporal Modeling