Predicative machine learning models are frequently being used by companies, institutes and organizations to make choices about humans. Strategic classification studies learning in settings where self-interested users can strategically modify their features to obtain favorable predictive outcomes. A key working assumption, however, is that 'favorable' always means 'positive'; this may be appropriate in some applications (e.g., loan approval, university admissions and hiring), but reduces to a fairly narrow view what user interests can be. In this work we argue for a broader perspective on what accounts for strategic user behavior, and propose and study a flexible model of generalized strategic classification. Our generalized model subsumes most current models, but includes other novel settings; among these, we identify and target one intriguing sub-class of problems in which the interests of users and the system are aligned. For this cooperative setting, we provide an in-depth analysis, and propose a practical learning approach that is effective and efficient. We compare our approach to existing learning methods and show its statistical and optimization benefits. Returning to our fully generalized model, we show how our results and approach can extend to the most general case. We conclude with a set of experiments that empirically demonstrate the utility of our approach.
翻译:公司、研究所和组织经常使用具有先验性的机器学习模式来作出有关人类的选择。战略分类研究在自我感兴趣的用户能够从战略上改变其特征以获得有利的预测结果的情况下进行学习。然而,一个关键的工作假设是,“喜好”总是意味着“积极”;这在某些应用(例如贷款批准、大学入学和聘用)中可能是适当的,但缩小到了相对狭窄的视角,即什么是用户的利益。在这项工作中,我们主张从更广泛的角度看待战略用户行为的核算,提出并研究一种灵活的通用战略分类模式。我们的普遍模式子集了大多数当前模式,但包括了其他新的环境;其中,我们确定并针对一个令人感兴趣的问题分类子类别,使用户和系统的利益相互一致。我们为这一合作环境提供了深入分析,并提出一种有效和高效的实用的学习方法。我们比较了我们的方法与现有的学习方法,并展示其统计和优化的效益。回到我们完全普遍化的模式,我们展示了我们的成果和办法如何能够扩大到最普遍的模式。我们以一套实验方法来完成我们的实验。