Strategic Classification in the Dark
Ganesh Ghalme,u00a0Vineet Nair,u00a0Itay Eilat,u00a0Inbal Talgam-Cohen,u00a0Nir Rosenfeld
Strategic classification studies the interaction between a classification rule and the strategic agents it governs. Agents respond by manipulating their features, under the assumption that the classifier is known. 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 and analyze the effect of an unknown classifier. We define the u201dprice of opacityu201d as the difference between the prediction error under the opaque and transparent policies, characterize it, and give a sufficient condition for it to be strictly positive, in which case transparency is the recommended policy. Our experiments show how Hardt et al.u2019s robust classifier is affected by keeping agents in the dark.
