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体，在这个社会中，公司收集和分析大量的个人信息，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。这个项目解决了这样一个问题：什么样的公平理念支撑着一个使用个人数据和他们过去的行为来决定谁会得到什么的经济体系？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、合法与不合法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，这些想法还没有确定下来。通过研究早期的大数据经济，这个项目试图捕捉市场公平的某些想法是如何被接受的？而其他人则被抛在一边。为了了解这些动态，该项目侧重于汽车保险定价的情况，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？在道德上规范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使信用评分、社交媒体帖子和实时驾驶数据等各种类型的信息的使用发生变化。该项目借鉴了政策和行业文件，深入访谈和保险会议上的人种学观察，以分析不同的维护公平的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的道德直觉。通过这种分析，该项目有助于经济社会学？该奖项反映了NSF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.