具有内生行为的公平预测
Fair Prediction with Endogenous Behavior
摘要：
越来越多的监管机构对部署在重要领域（例如刑事司法）的机器学习算法是否“公平地对待不同的人口群体”感兴趣。“然而，有几个拟议的公平概念，通常是相互不相容的。以刑事司法为例，我们研究了一个模型，在这个模型中，社会选择监禁规则。不同人口群体的代理人在其外部选择（例如合法就业机会）方面有所不同，并决定是否犯罪。我们发现，均衡的I型和II型错误跨组是一致的目标，最大限度地减少整体犯罪率;其他流行的公平观念不。
英文摘要：
There is increasing regulatory interest in whether machine learning algorithms deployed in consequential domains (e.g. in criminal justice) treat different demographic groups "fairly." However, there are several proposed notions of fairness, typically mutually incompatible. Using criminal justice as an example, we study a model in which society chooses an incarceration rule. Agents of different demographic groups differ in their outside options (e.g. opportunity for legal employment) and decide whether to commit crimes. We show that equalizing type I and type II errors across groups is consistent with the goal of minimizing the overall crime rate; other popular notions of fairness are not.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
