  Strategic classification studies the interaction between a classification
rule and the strategic agents it governs. Under the assumption that the
classifier is known, rational agents respond to it by manipulating their
features. However, in many real-life scenarios of high-stake classification
(e.g., credit scoring), the classifier is not revealed to the agents, which
leads agents to attempt to learn the classifier and game it too. In this paper
we generalize the strategic classification model to such scenarios. We define
the price of opacity as the difference in prediction error between opaque and
transparent strategy-robust classifiers, characterize it, and give a sufficient
condition for this price to be strictly positive, in which case transparency is
the recommended policy. Our experiments show how Hardt et al.'s robust
classifier is affected by keeping agents in the dark.
