Classification algorithms are increasingly used in areas such as housing, credit, and law enforcement in order to make decisions affecting peoples' lives. These algorithms can change individual behavior deliberately (a fraud prediction algorithm deterring fraud) or inadvertently (content sorting algorithms spreading misinformation), and they are increasingly facing public scrutiny and regulation. Some of these regulations, like the elimination of cash bail in some states, have focused on \textit{lowering the stakes of certain classifications}. In this paper we characterize how optimal classification by an algorithm designer can affect the distribution of behavior in a population -- sometimes in surprising ways. We then look at the effect of democratizing the rewards and punishments, or stakes, to algorithmic classification to consider how a society can potentially stem (or facilitate!) predatory classification. Our results speak to questions of algorithmic fairness in settings where behavior and algorithms are interdependent, and where typical measures of fairness focusing on statistical accuracy across groups may not be appropriate.

翻译：分类算法越来越多地应用于住房、信贷和执法等领域，以做出影响人们生活的决策。这些算法可能有意地改变个体行为（如欺诈预测算法阻止欺诈），也可能无意地改变行为（如内容排序算法传播错误信息），并且正日益面临公众审查和监管。某些监管措施，例如一些州取消现金保释，旨在降低特定分类的后果。在本文中，我们描述了算法设计者的最优分类如何影响群体中行为的分布——有时以令人惊讶的方式。然后，我们研究了将算法分类的奖惩或后果民主化的效果，以探讨社会如何可能遏制（或促进！）掠夺性分类。我们的研究结果涉及算法与行为相互依赖且典型的基于群体统计准确性的公平度量可能不适用的情境下的算法公平性问题。