  Local explanation frameworks aim to rationalize particular decisions made by
a black-box prediction model. Existing techniques are often restricted to a
specific type of predictor or based on input saliency, which may be undesirably
sensitive to factors unrelated to the model's decision making process. We
instead propose sufficient input subsets that identify minimal subsets of
features whose observed values alone suffice for the same decision to be
reached, even if all other input feature values are missing. General principles
that globally govern a model's decision-making can also be revealed by
searching for clusters of such input patterns across many data points. Our
approach is conceptually straightforward, entirely model-agnostic, simply
implemented using instance-wise backward selection, and able to produce more
concise rationales than existing techniques. We demonstrate the utility of our
interpretation method on various neural network models trained on text, image,
and genomic data.
