论文标题
鼓励建设性适应的线性分类器
Linear Classifiers that Encourage Constructive Adaptation
论文作者
论文摘要
机器学习系统通常用于个人适应其功能以获得预期结果的设置。在这种情况下，战略行为会导致部署模型绩效急剧下降。在这项工作中，我们旨在通过学习分类器来解决这个问题，这些分类器鼓励决策主体改变其特征，从而可以改善预测的\ emph {and}真实结果。我们将预测和适应性的动态构图为两阶段的游戏，并描述了模型设计师及其决策主题的最佳策略。在模拟和现实世界数据集的基准测试中，我们发现使用我们的方法训练的分类器保持了现有方法的准确性，同时诱发了更高水平的改进和更少的操作。
Machine learning systems are often used in settings where individuals adapt their features to obtain a desired outcome. In such settings, strategic behavior leads to a sharp loss in model performance in deployment. In this work, we aim to address this problem by learning classifiers that encourage decision subjects to change their features in a way that leads to improvement in both predicted \emph{and} true outcome. We frame the dynamics of prediction and adaptation as a two-stage game, and characterize optimal strategies for the model designer and its decision subjects. In benchmarks on simulated and real-world datasets, we find that classifiers trained using our method maintain the accuracy of existing approaches while inducing higher levels of improvement and less manipulation.
