Corporations gather massive amounts of personal data to predict how individuals will behave so that they can profitably price goods and allocate resources. This article investigates the moral foundations of such increasingly prevalent market practices. I leverage the case of credit scores in car insurance pricing—an early and controversial use of algorithmic prediction in the U.S. consumer economy—to unpack the premise that predictive data are fair to use and to understand the conditions under which people are likely to challenge that moral logic. Policymaker resistance to credit-based insurance scores reveals that contention arises when predictions depend on mathematical distinctions that do not align with broader understandings of good and bad behavior, and when theories about why predictions work point to the market holding people accountable for actions that are not really their fault. Via a de-commensuration process, policymakers realign the market with their own notions of moral deservingness. This article thus demonstrates the importance of causal understanding and moral categorization for people accepting markets as fair. As data and analytics permeate markets of all sorts, as well as other domains of social life, these findings have implications for how social scientists understand the novel forms of stratification that result.

中文翻译：

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预测实践的道德局限：基于信用的保险评分案例

公司收集大量个人数据来预测个人的行为方式，以便他们能够以有利可图的方式为商品定价并分配资源。本文调查了这种日益流行的市场实践的道德基础。我利用汽车保险定价中的信用评分案例——美国消费经济中算法预测的早期和有争议的使用——来解开预测数据可以公平使用的前提，并了解人们可能会挑战的条件道德逻辑。政策制定者对基于信用的保险评分的抵制表明，当预测依赖于与对好坏行为的更广泛理解不一致的数学区别时，就会出现争论，以及当关于为什么预测有效的理论指向市场要求人们对实际上不是他们的错的行为负责时。通过去公称化过程，政策制定者根据自己的道德价值观念重新调整市场。因此，本文展示了因果理解和道德分类对于接受市场公平的人们的重要性。随着数据和分析渗透到各种市场以及社会生活的其他领域，这些发现对社会科学家如何理解由此产生的新型分层形式具有重要意义。因此，本文展示了因果理解和道德分类对于接受市场公平的人们的重要性。随着数据和分析渗透到各种市场以及社会生活的其他领域，这些发现对社会科学家如何理解由此产生的新型分层形式具有重要意义。因此，本文展示了因果理解和道德分类对于接受市场公平的人们的重要性。随着数据和分析渗透到各种市场以及社会生活的其他领域，这些发现对社会科学家如何理解由此产生的新型分层形式具有重要意义。