论文标题
操纵机器学习
Manipulation-Proof Machine Learning
论文作者
论文摘要
越来越多的决策是通过机器学习算法来指导的。在许多情况下，从消费者信贷到刑事司法，这些决定是通过将估算器应用于个人观察到的行为的数据来做出的。但是，当相应的决定在规则中编码时，个人可能会在战略上改变其行为以实现预期的结果。本文开发了一种新的估算器，即使决策规则完全透明，该估计器也是稳定的。我们明确地对操纵不同行为的成本进行建模，并确定在平衡方面稳定的决策规则。通过肯尼亚的大型现场实验，我们表明，使用我们的策略稳定方法估计的决策规则优于基于标准监督学习方法的决策规则。
An increasing number of decisions are guided by machine learning algorithms. In many settings, from consumer credit to criminal justice, those decisions are made by applying an estimator to data on an individual's observed behavior. But when consequential decisions are encoded in rules, individuals may strategically alter their behavior to achieve desired outcomes. This paper develops a new class of estimator that is stable under manipulation, even when the decision rule is fully transparent. We explicitly model the costs of manipulating different behaviors, and identify decision rules that are stable in equilibrium. Through a large field experiment in Kenya, we show that decision rules estimated with our strategy-robust method outperform those based on standard supervised learning approaches.
