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This work addresses the situation where a black-box model with goodpredictive performance is chosen over its interpretable competitors, and weshow interpretability is still achievable in this case. Our solution is to findan interpretable substitute on a subset of data where the black-box model isoverkill or nearly overkill while leaving the rest to the black-box. Thistransparency is obtained at minimal cost or no cost of the predictiveperformance. Under this framework, we develop a Hybrid Rule Sets (HyRS) modelthat uses decision rules to capture the subspace of data where the rules are asaccurate or almost as accurate as the black-box provided. To train a HyRS, wedevise an efficient search algorithm that iteratively finds the optimal modeland exploits theoretically grounded strategies to reduce computation. Ourframework is agnostic to the black-box during training. Experiments onstructured and text data show that HyRS obtains an effective trade-off betweentransparency and interpretability.

Gaining Free or Low-Cost Transparency with Interpretable Partial Substitute