RAPID: Countering Language Biases in COVID-19 Search Auto-Completes

RAPID：应对 COVID-19 搜索自动完成中的语言偏见

• 批准号: 2027784
• 负责人: Vivek Singh
• 金额: $ 19.9万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027784&HistoricalAwards=false
• 关键词: RAPID; Countering; Language; Biases; COVID

The novel coronavirus (COVID-19) has resulted in sharp increases in online search activity about the disease, its spread, and remedial actions. Hence, search engines can significantly influence public perceptions of the disease and the actions undertaken by the public. If there are language biases in the results of searches, there may also be biases in perceptions and actions taken. This project will systematically analyze the differences in COVID-19 related search auto-completes that are provided to English and Spanish speakers. The results will generate new knowledge on the emergence of algorithmic bias and help ensure equity in health information dissemination at scale amid large-scale health emergencies. The findings will be shared in easy-to-understand terms on an urgent basis in multiple languages to help ensure equal access to health information in the COVID-19 pandemic. This feedback could help improve the health outcomes for numerous individuals facing the COVID-19 pandemic.The project is designed to yield approaches for countering language-based bias in COVID-19 related health information dissemination by search engines using log analysis and interviews. The first step in this project is to audit the search auto-complete results in Spanish and English, and test if there are systematic differences in the way results are generated across the two languages. The next step is to this utilize focus groups with multiple users to understand how auto-complete queries affect the way English versus Spanish speakers understand COVID-19 disease and take necessary precautions. The findings from the two phases are to be combined to generate guidelines on designing search experiences that support health equity amid a public health crisis. This research topic is likely to attract a diverse range of student researchers, which could help broaden participation in STEM research career pipelines.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.

新型冠状病毒（COVID-19）导致有关该疾病、其传播和补救措施的在线搜索活动急剧增加。因此，搜索引擎可以极大地影响公众对疾病的看法和公众采取的行动。如果搜索结果中存在语言偏见，那么所采取的看法和行动也可能存在偏见。该项目将系统地分析提供给英语和西班牙语使用者的COVID-19相关搜索自动完成的差异。研究结果将产生关于算法偏差出现的新知识，并有助于确保在大规模卫生紧急情况下大规模卫生信息传播的公平性。调查结果将以易于理解的方式以多种语言紧急分享，以帮助确保在COVID-19大流行中平等获得健康信息。该项目旨在通过日志分析和访谈，提出消除搜索引擎在传播COVID-19相关健康信息时基于语言的偏见的方法。该项目的第一步是审核西班牙语和英语的搜索自动完成结果，并测试两种语言的结果生成方式是否存在系统差异。下一步是利用多个用户的焦点小组来了解自动完成查询如何影响英语和西班牙语使用者理解COVID-19疾病的方式，并采取必要的预防措施。这两个阶段的研究结果将被结合起来，以产生设计搜索体验的指导方针，在公共卫生危机中支持卫生公平。该研究课题可能会吸引各种各样的学生研究人员，这可能有助于扩大STEM研究职业管道的参与。该奖项反映了NSF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。

项目成果

Understanding search autocompletes from the perspectives of English and Spanish speakers during the early months of the COVID‐19 pandemic

在 COVID-19 大流行的最初几个月，从讲英语和西班牙语的人的角度了解搜索自动完成功能

• DOI: 10.1002/jcop.23013
• 发表时间: 2023
• 期刊: Journal of Community Psychology
• 影响因子: 2.3
• 作者: Valera, Pamela;Carmona, David;Singh, Vivek;Malarkey, Sarah;Baquerizo, Humberto;Smith, Nadia
• 通讯作者: Smith, Nadia