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被用于访问金融服务。对于银行存款不足的人群来说尤其如此，他们依赖预付卡和移动应用程序来收发资金。在这个过程中，金融服务平台也在成为内容平台，而银行和信用社继续输给金融科技(金融科技)服务和APP，以及其他边缘服务。不幸的是，这种转变也助长了有关金融的虚假信息的传播。这个项目试图解决由于虚假信息对小型化社区造成的一种特殊危害，即被排除在金融服务之外的危害。几乎所有现有的关于虚假信息的研究都集中在政治传播上。大多数研究还聚焦于白人群体--特别是年轻的白人男性是虚假信息的制造者，老年白人群体是政治虚假信息的消费者和传播者。新的研究表明，在虚假信息的流通中存在种族不对称。认识到人类是在社会群体和认知社区中学习的社会生物，通过对不同结构网络中虚假信息的传播进行建模，可以洞察哪些社会结构对不良信息提供了最好的弹性。社会学习模型还可以帮助解释群体如何在大相径庭的话题或知识或信念领域信任其他群体成员。这样的模型可以为新的认知社区的设计提供信息。这个项目的重点是在美国种族主义和边缘化社区中关于金钱、银行和金融的虚假信息和错误信息。它寻求通过融合科学的方法来解决这种错误和错误的信息。将探讨解决这一问题的新形式的合作组织结构，其出发点是，虚假信息是一个社会问题，需要社会作出反应，包括建立新的机构来跟踪和打击虚假信息，并在这一进程中采取包容各方的民主参与的新形式。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，并将试行一个社区驱动的论坛，以a)监测虚假信息和虚假信息，并开发适当的方法来应对它；以及b)授权人们应对金融服务中迅速崛起的数字信用评分和其他算法过程。在这样做的过程中，信用合作社系统的合作模式将被用来作为民主实践的一部分，建立值得信赖和真实的信息共享模式。项目伙伴包括侧重于种族经济公平的社区组织，这些组织将提供他们的专门知识，并协助开发新的模式，以了解和处理虚假信息。将试行数字包容的示范基础设施，这将确保它本身不会成为一种掠夺性包容形式。将开发可信的信息流，以处理不信任问题。除了信用社网络，该项目合作伙伴关系还包括促进种族和经济公平的国家研究和行动研究所PolicyLink，以及增强微型家庭财务能力的Filene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势和更广泛的影响审查标准进行评估，被认为值得支持。