CHS: Small: Exploring and Predicting Unintended Consequences of Technology

CHS：小：探索和预测技术的意外后果

• 批准号: 2006104
• 负责人: Katharina Reinecke
• 金额: $ 50万
• 依托单位: University of Washington
• 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: 2020-07-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2006104&HistoricalAwards=false
• 关键词: CHS; Small; Exploring; Predicting; Unintended

This award investigates the unintended consequences of Internet technologies that people have experienced in the past and that may arise in the future. Leveraging the "wisdom of the crowd," the work will give a voice to people across the US, including underrepresented populations, by surfacing how technology products have affected themselves and their communities - both positively and negatively - beyond the intended purposes. The work will also provide tools and predictive models that help product teams anticipate the side effects that new technologies might have on specific user groups and their communities. The research includes (1) a large-scale study on the principal investigator's LabintheWild platform to elicit qualitative reflections on a set of existing technology inventions; (2) automating the analysis of this data using unsupervised structural topic modeling to determine how different demographic groups perceive current technology and to relate themes of unintended consequences to specific categories of technology; and (3) developing a volunteer-powered, data-driven tool that provides a lens into the views of broader society and supports companies in assessing and foreseeing potential unintended positive and negative consequences without needing to reveal proprietary ideas. The tool will automatically update its predictions based on newly collected data to remain useful as society moves on and new technology inventions appear. Educational components include a new course module on societal implications and ethics of technology, involving students in all parts of the research, and visualizations of unintended consequences that will be developed for scientific outreach at middle and high schools and for public access.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.

该奖项旨在调查人们过去经历过的以及未来可能出现的互联网技术的意外后果。这项工作将利用“群众的智慧”，通过展示技术产品如何影响他们自己及其社区--无论是积极的还是消极的--超出预期目的，为美国各地的人们，包括代表性不足的人群发出声音。这项工作还将提供工具和预测模型，帮助产品团队预测新技术可能对特定用户群体及其社区产生的副作用。该研究包括：（1）在首席研究员LabintheWild平台上进行大规模研究，以引发对一系列现有技术发明的定性思考;（2）使用无监督结构主题模型自动分析这些数据，以确定不同人口群体如何看待当前技术，并将意外后果的主题与特定类别的技术联系起来;以及（3）开发一个由志愿者提供动力的数据驱动工具，为更广泛的社会观点提供一个透镜，并支持公司评估和预防潜在的意外积极和消极后果，而无需透露专有想法。 该工具将根据新收集的数据自动更新其预测，以便随着社会的发展和新技术发明的出现而保持有用。 教育部分包括一个关于技术的社会影响和伦理的新课程模块，让学生参与研究的各个部分，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进行评估，被认为值得支持的搜索.

项目成果

How Language Formality in Security and Privacy Interfaces Impacts Intended Compliance

安全和隐私界面中的语言形式如何影响预期的合规性

• 发表时间: 2023
• 期刊: Human factors in computing systems
• 作者: Jackson Stokes;Tal August;Robert Marver;Alexei Czeskis;Franziska Roesner;Tadayoshi Kohno;Katharina Reinecke
• 通讯作者: Katharina Reinecke

Gendered Mental Health Stigma in Masked Language Models

蒙面语言模型中的性别心理健康耻辱

• 发表时间: 2022
• 期刊: Conference on Empirical Methods in Natural Language Processing
• 作者: Inna Wanyin Lin;Lucille Njoo;Anjalie Field;Ashish Sharma;Katharina Reinecke;Tim Althoff;Yulia Tsvetkov
• 通讯作者: Yulia Tsvetkov