  This work introduces interpretable regional descriptors, or IRDs, for local,
model-agnostic interpretations. IRDs are hyperboxes that describe how an
observation's feature values can be changed without affecting its prediction.
They justify a prediction by providing a set of "even if" arguments
(semi-factual explanations), and they indicate which features affect a
prediction and whether pointwise biases or implausibilities exist. A concrete
use case shows that this is valuable for both machine learning modelers and
persons subject to a decision. We formalize the search for IRDs as an
optimization problem and introduce a unifying framework for computing IRDs that
covers desiderata, initialization techniques, and a post-processing method. We
show how existing hyperbox methods can be adapted to fit into this unified
framework. A benchmark study compares the methods based on several quality
measures and identifies two strategies to improve IRDs.
