Corporations increasingly use personal data to offer individuals different products and prices. I present first-of-its-kind evidence about how U.S. consumers assess the fairness of companies using personal information in this way. Drawing on a nationally representative survey that asks respondents to rate how fair or unfair it is for car insurers and lenders to use various sorts of information—from credit scores to web browser history to residential moves—I find that everyday Americans make strong moral distinctions among types of data, even when they are told data predict consumer behavior (insurance claims and loan defaults, respectively). Open-ended responses show that people adjudicate fairness by drawing on shared understandings of whether data are logically related to the predicted outcome and whether the categories companies use conflate morally distinct individuals. These findings demonstrate how dynamics long studied by economic sociologists manifest in legitimating a new and important mode of market allocation.

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哪些数据可以公平区分？美国对在两种市场环境中使用个人数据的看法

公司越来越多地使用个人数据为个人提供不同的产品和价格。我提供了关于美国消费者如何评估公司以这种方式使用个人信息的公平性的首创证据。根据一项具有全国代表性的调查，该调查要求受访者评估汽车保险公司和贷方使用各种信息（从信用评分到网络浏览器历史再到住宅搬迁）的公平或不公平程度，我发现日常美国人在数据类型，即使他们被告知数据可以预测消费者行为（分别是保险索赔和贷款违约）。开放式回答表明，人们通过对数据是否与预测结果在逻辑上相关以及公司使用的类别是否将道德上不同的个体混为一谈的共同理解来判断公平性。这些发现表明，经济社会学家长期研究的动态如何体现在使一种新的重要市场分配模式合法化方面。