CAREER: Accountable Democracy: Mathematical Reasoning and Democratic Processes in America

近年来，越来越明显的是，计算技术已深深嵌入美国民主进程的每一个阶段。突出的例子包括计算重划选区和在投票和预测中增加使用统计分析。这个项目将把计算机、算法思维和数学原理在20世纪美国代议制民主宪法中的作用历史化。甚至在数字计算机和自动决策普及之前，关于“公平”、“偏见”和“歧视”的数学定义的争论就在美国生活中比比皆是，而这些观点在民主决策领域的辩论最为激烈。本项目着眼于国会分配、人口普查、比例投票、国会重新划分选区、投票和预测，研究了从统计建模到计算几何等不断变化的计算实践如何影响了20世纪美国选民对“公平”的最基本定义。本项目将运用科学技术研究的理论见解，将档案研究和口述历史相结合，以调查数学和计算指标以及法律、立法和社会指标在多大程度上决定了什么是“偏见”、“公平”和“歧视”。因此，本研究将阐明公平的技术和社会定义如何与计算和数学思维方式纠缠在一起。此外，它将考虑到公众对科学的理解，特别是公众对数学和统计学的理解。最后，它将扩展关于当专家的技能是一种方法（如统计）而不是一个主题（如气候变化，DNA指纹）时如何接受和裁决科学专业知识的研究。该项目将通过揭示和清晰地概述数学和计算知识有时掩盖政治和社会议程的方式，增加公众对科学和技术的参与。该项目的主要目标之一是了解数学对真理的主张是如何不仅在法庭上，而且在公众中被裁决的。将审查的一些辩论，如关于人口普查调整的辩论，本质上是高度技术性的。审视美国记者和公众为沟通和传播这些复杂问题而制定的成功（以及不成功）策略，对当代社会至关重要，因为日常生活的许多方面都受到技术上错综复杂（如果不是完全不透明的话）的计算系统的控制。随着数据分析越来越成为美国人参与最基本的民主宪法的基础，公开传播信息和工具包来帮助公民评估这些主张和他们的假设是至关重要的。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。

In recent years, it has become increasingly apparent that computational techniques are deeply embedded in every stage of the American democratic process. Prominent examples include computational redistricting and the increased use of statistical analysis in polling and forecasts. This project will historicize the role of computers, as well as algorithmic thinking and mathematical rationales, in the constitution of American representative democracy in the twentieth century. Even before digital computers and automated decision making became prevalent, controversies about mathematical definitions of “fairness,” “bias,” and “discrimination” abounded in American life, and nowhere were these ideas debated more forcefully than in the realm of democratic decision making. Focusing on congressional apportionment, the census, proportional voting, congressional redistricting, polling, and forecasts, this project investigates how changing computational practices, from statistical modeling to computational geometry, have insinuated themselves into the most basic definitions of “fairness” in the American electorate in the twentieth century. Applying theoretical insights from Science and Technology Studies, this project will integrate archival research and oral histories in order to investigate the extent to which ideas about what constitutes “bias,” “fairness,” and “discrimination” are determined by mathematical and computational metrics as well as by legal, legislative, and social ones. This research will therefore elucidate how technical and social definitions of fairness are entangled in computational and mathematical ways of thinking. Furthermore, it will reckon with the public understanding of science and, in particular, the public understanding of mathematics and statistics. Finally, it will extend the study of how scientific expertise is received and adjudicated when the expert’s skill is a method (e.g., statistics), rather than a subject matter (e.g., climate change, DNA fingerprinting). The project will increase public engagement with science and technology by unpacking and clearly outlining the ways in which mathematical and computational claims to knowledge have at times obscured political and social agendas. One of the primary goals of the project is to understand how mathematical claims to truth are adjudicated not only in the courts, but also by the general public. Some of the debates that will be examined, like the one regarding census adjustment, are highly technical in nature. Examining the successful (as well as unsuccessful) strategies that U.S. journalists and the public have developed to communicate and disseminate these complex issues is crucial to contemporary society, when so many aspects of daily life are governed by technically intricate, if not entirely opaque, computational systems. As data analytics become increasingly foundational to how Americans engage with the most basic constitution of democracy, it is of the utmost importance to publicly disseminate information and toolkits to help citizens evaluate such claims and their assumptions.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.