When users stand to gain from certain predictions, they are prone to act
strategically to obtain favorable predictive outcomes. Whereas most works on
strategic classification consider user actions that manifest as feature
modifications, we study a novel setting in which users decide -- in response to
the learned classifier -- whether to at all participate (or not). For learning
approaches of increasing strategic awareness, we study the effects of
self-selection on learning, and the implications of learning on the composition
of the self-selected population. We then propose a differentiable framework for
learning under self-selective behavior, which can be optimized effectively. We
conclude with experiments on real data and simulated behavior that both
complement our analysis and demonstrate the utility of our approach.

用户的自主选择行为对学习过程和学习结果有重要影响，在预测中用户会采取策略性的行动，我们研究了一种用户根据学习到的分类器决定是否参与的新型情景，为了提高学习的策略性，我们提出了一个可有效优化的自主选择学习框架，并通过实验验证了其实用性。

战略自我选择下的分类

Classification Under Strategic Self-Selection

When the residents of Flint learned that lead had contaminated their water
system, the local government made water-testing kits available to them free of
charge. The city government published the results of these tests, creating a
valuable dataset that is key to understanding the causes and extent of the lead
contamination event in Flint. This is the nation's largest dataset on lead in a
municipal water system.
In this paper, we predict the lead contamination for each household's water
supply, and we study several related aspects of Flint's water troubles, many of
which generalize well beyond this one city. For example, we show that elevated
lead risks can be (weakly) predicted from observable home attributes. Then we
explore the factors associated with elevated lead. These risk assessments were
developed in part via a crowd sourced prediction challenge at the University of
Michigan. To inform Flint residents of these assessments, they have been
incorporated into a web and mobile application funded by \texttt{Google.org}.
We also explore questions of self-selection in the residential testing program,
examining which factors are linked to when and how frequently residents
voluntarily sample their water.

本文从 Flint 居民自愿水检测的数据中，针对居民特征预测铅污染水源的可能性，探讨与污染扩散相关的因素，并针对社区开发基于互联网和移动应用的风险评估工具。同时，对居民自愿参与水检测的自我选择原因进行考察。