Classification Under Strategic Self-Selection

When users stand to gain from certain predictive outcomes, they are prone to act strategically to In this work we study classification of strategic agents that obtain predictions that are favorable. Most current choose whether to apply or not in response to the learned works consider strategic behavior that manifests classifier. Strategic candidates apply only if the expected as users modifying their features; instead, we utility from passing screening outweighs associated costs; study a novel setting in which users decide thus, application choices derive from beliefs regarding classification whether to even participate (or not), this in response outcomes. Since these choices in aggregate determine to the learned classifier. Considering learning the test-time distribution, learning becomes susceptible approaches of increasing strategic awareness, to self-selection--namely selection that is carried out by the we investigate the effects of user self-selection agents which predictions target. Our goal in this paper is to on learning, and the implications of learning on study learning under such self-selective behavior, which we the composition of the self-selected population.