CHS: Large: Collaborative Research: Pervasive Data Ethics for Computational Research

CHS：大型：协作研究：计算研究的普遍数据伦理

• 批准号: 1704598
• 负责人: Matthew Bietz
• 金额: $ 41.54万
• 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: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1704598&HistoricalAwards=false
• 关键词: CHS; Large; Collaborative; Research; Pervasive

This project promotes the progress of science and technology development by providing the empirical knowledge needed to advance fair, just computational research. Big, pervasive data about people enables fundamentally new computational research, but also raises new ethical challenges, such as accounting for distributed harms at scale, protecting against the risks of unpredictable future uses of data, and ensuring fairness in automated decision-making. National debates have erupted over online experiments, leaked datasets, and the definition of "public" data. Investigators struggle to advise students on engaging vulnerable populations or navigating terms of service. Regulators debate how to translate traditional ethical principles into workable policy guidance. Research addressing these challenges has hit roadblocks caused by a lack of empirical knowledge about emerging norms and expectations. This project discovers how diverse stakeholders - big data researchers, platforms, regulators, and user communities - understand their ethical obligations and choices, and how their decisions impact data system design and use. It also compares stakeholder perspectives against the risks and realities of pervasive data itself, answering fundamental questions about the fairness and ethics of such research. Understanding how computing researchers adapt their practices in the big data era, and highlighting points of convergence or conflict with data realities, user expectations, and regulatory practices, will produce concrete guidance for pervasive data ethics. In addition to improving ethical approaches for studying people in computing contexts, this work empowers researchers with actionable information about emergent norms and risks. Outputs, such as decision-support tools, guidance on measuring risk, public educational material and bibliographies, and reusable empirical data, are designed to support the wide range of stakeholders in data ethics. To meet these goals, this project enables a collaboratory - a virtual center combining data and analytical resources - to collect empirical data on research ethics at diverse scopes and scales. The research includes including attention to multiple ethical issues (privacy, risk, respect, beneficence, justice) as well as the full network of stakeholders involved in research ethics (user communities, computing research communities, technical platforms, and regulations). The project conducts interviews with, and surveys of, 1) user communities, 2) computing researchers, 3) data ethics regulators, and 4) commercial platform providers. The project also gathers numerous shared document sets, including 1) pervasive data research publications, 2) pervasive computing curricula and degree requirements, 3) news articles and public discourse about pervasive data research, 4) a corpus of existing data ethics training, 5) pervasive data grant summaries and data management plans, and 6) corporate ethics guidelines and regulatory documents. The project uses these resources to: discover metrics for assessing and moderating risks to data subjects; document how user attitudes and media reactions shape subjects' willingness to participate in pervasive data research; model user concerns in ways accessible to computational researchers; discover how existing ethical codes can be adapted and adopted for the real-world working conditions of sociotechnical and cyber-human research; determine how the changing practices of academic and corporate regulators impact users and researchers; and illuminate implementable and sustainable best practices for research ethics.

该项目通过提供推进公平、公正的计算研究所需的经验知识，促进科学和技术发展的进步。 关于人的大而普遍的数据使全新的计算研究成为可能，但也带来了新的道德挑战，例如大规模的分布式危害，防止未来不可预测的数据使用风险，以及确保自动化决策的公平性。全国性的辩论已经爆发了在线实验，泄露的数据集和“公共”数据的定义。调查人员很难就如何吸引弱势群体或如何浏览服务条款向学生提供建议。监管机构就如何将传统的道德原则转化为可行的政策指导展开辩论。应对这些挑战的研究遇到了障碍，原因是缺乏对新出现的规范和期望的经验知识。该项目旨在探索不同的利益相关者（大数据研究人员、平台、监管机构和用户社区）如何理解他们的道德义务和选择，以及他们的决策如何影响数据系统的设计和使用。它还将利益相关者的观点与普遍存在的数据本身的风险和现实进行比较，回答了有关此类研究的公平性和道德的基本问题。了解计算研究人员如何在大数据时代调整他们的实践，并强调与数据现实，用户期望和监管实践的融合或冲突点，将为普遍的数据伦理提供具体的指导。除了改进在计算环境中研究人类的道德方法外，这项工作还为研究人员提供了有关紧急规范和风险的可操作信息。决策支持工具、风险衡量指南、公共教育材料和参考书目以及可重复使用的经验数据等产出旨在为数据伦理方面的广泛利益攸关方提供支持。为了实现这些目标，该项目使一个合作实验室-一个虚拟中心相结合的数据和分析资源-收集经验数据的研究伦理在不同的范围和规模。该研究包括关注多个伦理问题（隐私，风险，尊重，善行，正义）以及参与研究伦理的利益相关者（用户社区，计算研究社区，技术平台和法规）的完整网络。该项目对1）用户社区，2）计算研究人员，3）数据道德监管机构和4）商业平台提供商进行了采访和调查。该项目还收集了许多共享文档集，包括1）普适数据研究出版物，2）普适计算课程和学位要求，3）关于普适数据研究的新闻文章和公共话语，4）现有数据伦理培训的语料库，5）普适数据授予摘要和数据管理计划，以及6）企业道德准则和监管文件。该项目利用这些资源来：发现评估和缓和数据主体风险的指标;记录用户态度和媒体反应如何影响主体参与普遍数据研究的意愿;以计算研究人员可以使用的方式模拟用户关注的问题;发现现有道德守则如何适应和采用社会技术和网络人类研究的现实工作条件;确定学术和企业监管机构不断变化的做法如何影响用户和研究人员;并阐明可实施和可持续的研究道德最佳实践。

项目成果

Excavating awareness and power in data science: A manifesto for trustworthy pervasive data research

挖掘数据科学的意识和力量：值得信赖的普适数据研究宣言

• DOI: 10.1177/20539517211040759
• 发表时间: 2021
• 期刊: Big Data & Society
• 影响因子: 8.5
• 作者: Shilton, Katie;Moss, Emanuel;Gilbert, Sarah A.;Bietz, Matthew J.;Fiesler, Casey;Metcalf, Jacob;Vitak, Jessica;Zimmer, Michael
• 通讯作者: Zimmer, Michael