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Strategic classification studies learning in settings where self-interestedusers can strategically modify their features to obtain favorable predictiveoutcomes. A key working assumption, however, is that "favorable" always means"positive"; this may be appropriate in some applications (e.g., loan approval),but reduces to a fairly narrow view of what user interests can be. In this workwe argue for a broader perspective on what accounts for strategic userbehavior, and propose and study a flexible model of generalized strategicclassification. Our generalized model subsumes most current models but includesother novel settings; among these, we identify and target one intriguingsub-class of problems in which the interests of users and the system arealigned. This setting reveals a surprising fact: that standard max-marginlosses are ill-suited for strategic inputs. Returning to our fully generalizedmodel, we propose a novel max-margin framework for strategic learning that ispractical and effective, and which we analyze theoretically. We conclude with aset of experiments that empirically demonstrate the utility of our approach.

Generalized Strategic Classification and the Case of Aligned Incentives