  Given a black-box classification model and an unlabeled evaluation dataset
from some application domain, efficient strategies need to be developed to
evaluate the model. Random sampling allows a user to estimate metrics like
accuracy, precision, and recall, but may not provide insight to high-confidence
errors. High-confidence errors are rare events for which the model is highly
confident in its prediction, but is wrong. Such errors can represent costly
mistakes and should be explicitly searched for. In this paper we propose a
generalization to the Adversarial Distance search that leverages concepts from
adversarial machine learning to identify predictions for which a classifier may
be overly confident. These predictions are useful instances to sample when
looking for high-confidence errors because they are prone to a higher rate of
error than expected. Our generalization allows Adversarial Distance to be
applied to any classifier or data domain. Experimental results show that the
generalized method finds errors at rates greater than expected given the
confidence of the sampled predictions, and outperforms competing methods.
