  Imagine being able to ask questions to a black box model such as "Which
adversarial examples exist?", "Does a specific attribute have a
disproportionate effect on the model's prediction?" or "What kind of
predictions could possibly be made for a partially described example?" This
last question is particularly important if your partial description does not
correspond to any observed example in your data, as it provides insight into
how the model will extrapolate to unseen data. These capabilities would be
extremely helpful as they would allow a user to better understand the model's
behavior, particularly as it relates to issues such as robustness, fairness,
and bias. In this paper, we propose such an approach for an ensemble of trees.
Since, in general, this task is intractable we present a strategy that (1) can
prune part of the input space given the question asked to simplify the problem;
and (2) follows a divide and conquer approach that is incremental and can
always return some answers and indicates which parts of the input domains are
still uncertain. The usefulness of our approach is shown on a diverse set of
use cases.
