Doctoral Dissertation Research: The Moral Foundations of the Big Data Economy

美国越来越成为一个大数据经济，在这个社会中，企业收集和分析大量的个人信息，以预测个人的行为，以便他们能够更有利可图地为商品和服务定价，并分配经济资源，如保险、信贷和就业。这个项目解决了这样一个问题：一个利用个人及其过去行为的数据来决定谁能得到未来的东西的经济体系，是什么样的公平观念支撑着这个体系？长期以来，经济社会学家一直在研究市场和决定市场轮廓的人，如商业领袖和监管者，是如何将对好与坏、合法与不适当的某些理解制度化的。随着时间的推移，这些关于市场应该如何运作的想法变成了理所当然的假设，似乎是自然和显而易见的，但当市场实践是新的时候，这些想法还没有确定下来。通过研究早年的大数据经济，该项目试图捕捉市场公平的某些理念是如何被接受的。其他人则被抛在一边。为了了解这些动态，该项目将重点放在汽车保险定价的案例上，并利用一系列美国政策辩论来研究市场参与者如何？包括公司、监管机构和消费者吗？道德框架在保险定价中使用个人数据。该项目还研究了不同的道德框架如何促进或改变对各种信息的使用，如信用评分、社交媒体帖子和实时驾驶数据。该项目借鉴了政策和行业文件、深度访谈和保险会议上的人种学观察，分析了不同的公平解释方式如何导致不同的商业实践和政策结果。为了补充这些数据，该项目还包括对美国消费者的调查，以了解生活受到这些政策和做法影响的人们的道德直觉。通过这种分析，该项目对经济社会学做出了贡献。美国的道德和市场文学，以及理解大规模数据收集、预测分析和算法决策实践的新兴努力，这些实践日益塑造了市场。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Increasingly the United States is a big data economy, a society in which corporations gather and analyze massive amounts of personal information to predict how individuals will behave so that they can more profitably price goods and services and allocate economic resources like insurance, credit, and jobs. This project addresses the question: What ideas about fairness underpin an economic system that uses data about individuals and their past behavior to determine who gets what going forward? Economic sociologists have long studied how markets and the people who decide their contours, like business leaders and regulators, institutionalize certain understandings of good and bad, of legitimate and inappropriate. Over time, these ideas about how markets should operate become taken-for-granted assumptions that seem natural and obvious, but when market practices are new, these ideas are not yet settled on. By studying the big data economy in its early years, this project seeks to capture how certain ideas about market fairness are becoming accepted?and others are being left aside. To understand these dynamics, the project focuses on the case of car insurance pricing and leverages a series of U.S. policy debates to study how market actors?including companies, regulators, and consumers?morally frame the use of personal data in insurance pricing. The project also studies how different moral frames enable or deflect the use of various types of information, such as credit scores, social media posts, and real-time driving data. The project draws from policy and industry documents, in-depth interviews, and ethnographic observations at insurance conferences to analyze how different ways of construing fairness lead to different business practices and policy outcomes. To complement these data, the project includes a survey of American consumers to understand the moral intuitions of the people whose lives are affected by these policies and practices. With this analysis, the project contributes to economic sociology?s morals and markets literature and burgeoning efforts to understand the practices of mass data collection, predictive analytics, and algorithmic decision-making, which increasingly give markets their shape.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.