NSF Convergence Accelerator Track F: An Algorithmic Observatory to Address Financial Misinformation and Disinformation in Minoritized Communities (LOI ID: L02616265)

NSF 融合加速器轨道 F：解决少数群体中的财务错误信息和虚假信息的算法观察站（LOI ID：L02616265）

• 批准号: 2137567
• 负责人: William Maurer
• 金额: $ 75万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2137567&HistoricalAwards=false
• 关键词: NSF; Convergence; Accelerator; Track; Algorithmic

Smartphones and other mobile platforms are increasingly being used to access financial services. This is especially the case for the underbanked population who rely on prepaid cards and mobile applications to send and receive funds. In this process, financial service platforms are also becoming content platforms, while banks and credit unions continue to lose ground to financial technology (fintech) services and apps, and to other fringe services. Unfortunately, this shift is also contributing to the spread of disinformation about finance. This project seeks to address a particular harm to minoritized communities arising as a result of disinformation, viz, the harm of being excluded from financial services.Almost all extant research on disinformation has focused on political communications. Most studies have also focused on the white population—in particular, younger white men as producers of disinformation and the older white population as consumers and spreaders of political disinformation. New research suggests that there are racial asymmetries in the circulation of disinformation. Recognizing that humans are social creatures who learn in social groupings and epistemic communities, modeling the spread of disinformation in networks of varying structures provides insight into which social structures best provide resilience against bad information. Social learning models can also help explain how groups can place trust in other group members on widely divergent topics or domains of knowledge or belief. Such models can inform the design of new epistemic communities. This project focuses on disinformation and misinformation about money, banking, and finance among racialized and marginalized communities in the United States. It seeks to address such dis- and misinformation through a convergence science approach. New forms of cooperative organizational structures will be explored to address the issue, proceeding from the notion that disinformation is a social problem requiring a societal response, including new institutions to track and confront disinformation and new forms of inclusive democratic participation in the process. The project will partner with the U.S. credit union system as a laboratory for cooperative democracy and will pilot a community-driven forum for a) monitoring misinformation and disinformation and developing appropriate approaches to counter it; and b) empowering people to deal with the rapid rise of digital credit scoring and other algorithmic processes in financial services. In doing so, the cooperative model of the credit union system will be harnessed for modeling trustworthy and authentic information sharing as part of democratic practice. Project partners include community organizations focused on racial economic equity who will provide their expertise and assist in the development of new models for understanding and addressing disinformation. A model infrastructure for digital inclusion will be piloted, that will ensure that it does not itself become a form of predatory inclusion. Trusted information flows will be developed that deal with issues of mistrust. In addition to the credit union network, the project partnership also includes PolicyLink, a national research and action institute advancing racial and economic equity, and the Filene Research Institute for enhancing the financial capabilities of minoritized households.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.

智能手机和其他移动平台越来越多地用于获取金融服务，尤其是那些依赖预付卡和移动应用程序发送和接收资金的银行服务不足的人群。在此过程中，金融服务平台也正在成为内容平台。不幸的是，银行和信用合作社继续败给金融科技服务和应用程序以及其他边缘服务，这种转变也助长了有关金融的虚假信息的传播。由于以下原因而产生的社区虚假信息，即被排除在金融服务之外的危害。几乎所有现有的虚假信息研究都集中在政治传播上，大多数研究也集中在白人群体上，特别是作为虚假信息制造者的年轻白人和老年白人群体。新的研究表明，虚假信息的传播存在种族不对称性，认识到人类是在社会群体和认知社区中学习的社会生物，对不同结构的网络中虚假信息的传播进行建模可以提供帮助。深入了解哪种社会结构最能提供抵御不良信息的能力。社会学习模型还可以帮助解释群体如何在广泛不同的主题或知识或信仰领域信任其他群体成员。这种模型可以为新的认知社区的设计提供信息。该项目重点关注美国种族化和边缘化社区中有关货币、银行和金融的虚假信息和错误信息，旨在通过融合科学方法解决此类虚假信息和错误信息，以探索新形式的合作组织结构。问题从概念出发虚假信息是一个社会问题，需要社会做出反应，包括建立新的机构来跟踪和对抗虚假信息，以及在此过程中包容性民主参与的新形式。该项目将与美国信用合作社系统合作，作为合作民主的实验室，并将试点。社区驱动的论坛，目的是 a) 监控错误信息和虚假信息并制定适当的方法来应对；b) 使人们能够应对金融服务中数字信用评分和其他算法流程的快速增长。信用合作社系统将用于建立可信和真实的信息共享模型，作为民主实践的一部分。项目合作伙伴包括关注种族经济平等的社区组织，他们将提供专业知识并协助开发理解和解决虚假信息的新模型。 ，这将确保它本身不会成为一种掠夺性包容的形式。除了信用合作社网络之外，该项目合作伙伴还包括国家研究和行动机构PolicyLink。促进种族和经济平等以及法林研究旨在提高少数族裔家庭财务能力的研究所。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。