  We present a novel subset scan method to detect if a probabilistic binary
classifier has statistically significant bias -- over or under predicting the
risk -- for some subgroup, and identify the characteristics of this subgroup.
This form of model checking and goodness-of-fit test provides a way to
interpretably detect the presence of classifier bias or regions of poor
classifier fit. This allows consideration of not just subgroups of a priori
interest or small dimensions, but the space of all possible subgroups of
features. To address the difficulty of considering these exponentially many
possible subgroups, we use subset scan and parametric bootstrap-based methods.
Extending this method, we can penalize the complexity of the detected subgroup
and also identify subgroups with high classification errors. We demonstrate
these methods and find interesting results on the COMPAS crime recidivism and
credit delinquency data.
