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

美国日益成为一个大数据经济社会，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，并分配保险、信贷和就业等经济资源。这个项目解决的问题是：什么关于公平的想法支撑着一个经济体系，这个经济体系使用关于个人及其过去行为的数据来决定谁在未来得到什么？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，如商界领袖和监管者，是如何将某些对好与坏、对合法与不合适的理解制度化的。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，这些假设似乎是自然和显而易见的，但当市场实践是新的时，这些想法还没有确定下来。通过研究早期的大数据经济，这个项目试图捕捉到关于市场公平的某些观点是如何被接受的？而其他一些观点则被搁置一边。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--包括公司、监管机构和消费者--如何在道德上构建个人数据在保险定价中的使用。该项目还研究了不同的道德框架如何支持或转移对各种类型信息的使用，如信用评分、社交媒体帖子和实时驾驶数据。该项目利用政策和行业文件、深度访谈和保险会议上的人种学观察，分析解释公平的不同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，该项目还对美国消费者进行了一项调查，以了解那些生活受到这些政策和做法影响的人的道德直觉。通过这一分析，该项目为经济社会学-S道德和市场文献做出了贡献，并正在努力理解海量数据收集、预测分析和算法决策的实践，这些实践越来越多地塑造了市场。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智力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.