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: 1947754
• 负责人: Michael Zimmer
• 金额: $ 27.69万
• 依托单位: Marquette University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1947754&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.

该项目通过提供公平，仅计算研究所需的经验知识来促进科学和技术发展的进步。 关于人们的大而普遍的数据可以从根本上实现新的计算研究，但也提出了新的伦理挑战，例如计算大规模分布式危害，防止无法预测的未来数据使用的风险，并确保自动决策的公平性。全国辩论对在线实验，数据集泄漏以及“公共”数据的定义爆发。调查人员努力就吸引弱势群体或导航服务条款的建议。监管机构辩论如何将传统的道德原则转化为可行的政策指导。解决这些挑战的研究袭击了由于缺乏有关新兴规范和期望的经验知识而引起的障碍。该项目发现了多样化的利益相关者 - 大数据研究人员，平台，监管机构和用户社区如何了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数据本身的风险和现实进行了比较，回答了有关此类研究的公平性和道德规范的基本问题。了解计算研究人员如何在大数据时代适应其实践，并突出融合或与数据现实，用户期望和监管实践冲突的点，将为普遍数据道德提供具体的指导。除了改善在计算环境中研究人员的道德方法外，这项工作还使研究人员能够获得有关紧急规范和风险的可行信息。诸如决策支持工具，衡量风险指南，公共教育材料和书目的指南以及可重复使用的经验数据之类的输出旨在支持数据伦理学中的广泛利益相关者。为了满足这些目标，该项目使一个合作中心（结合数据和分析资源的虚拟中心）能够收集有关各种范围和量表的研究伦理的经验数据。该研究包括关注多个道德问题（隐私，风险，尊重，福利，正义）以及参与研究道德的利益相关者的完整网络（用户社区，计算研究社区，技术平台和法规）。该项目对1）用户社区进行访谈和调查，2）计算研究人员，3）数据伦理调节器和4）商业平台提供商。该项目还收集了许多共享的文件集，包括1）普遍数据研究出版物，2）普遍的计算计算课程和学位要求，3）关于普遍数据研究的新闻文章和公众讨论，4）现有数据伦理学培训的语料库，5）普遍存在的数据授权数据赠款和数据管理计划，以及6）公司的伦理学指南和监管机构。该项目使用以下资源来：发现用于评估和调节数据主体风险的指标；记录用户态度和媒体反应如何塑造受试者参与普遍数据研究的意愿；以计算研究人员可以访问的方式建模用户关注；发现如何将现有的道德法规适应和采用，以适应社会技术和网络人类研究的现实工作条件；确定学术和公司监管机构的不断变化的实践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实践。

项目成果

Feature creep or just plain creepy? How advances in “smart” technologies affect attitudes toward data privacy

功能令人毛骨悚然还是只是令人毛骨悚然？

• 发表时间: 2020
• 期刊: Annals of the International Communication Association
• 作者: Vitak, J.

Public Concern About Monitoring Twitter Users and Their Conversations to Recruit for Clinical Trials: Survey Study

• DOI: 10.2196/15455
• 发表时间: 2019-10-30
• 期刊: JOURNAL OF MEDICAL INTERNET RESEARCH
• 影响因子: 7.4
• 作者: Reuter, Katja;Zhu, Yifan;Zimmer, Michael
• 通讯作者: Zimmer, Michael

ETHICAL REVIEW BOARDS AND PERVASIVE DATA RESEARCH: GAPS AND OPPORTUNITIES

道德审查委员会和普遍的数据研究：差距和机遇

• DOI: 10.5210/spir.v2020i0.11369
• 发表时间: 2020
• 期刊: AoIR Selected Papers of Internet Research
• 作者: Zimmer, Michael;Chapman, Edward
• 通讯作者: Chapman, Edward

Studying reddit: a systematic overview of disciplines, approaches, methods, and ethics

• DOI: 10.1177/20563051211019004
• 发表时间: 2021-01-01
• 期刊: Soc Media Soc.
• 作者: Proferes, N;Jones, N;Zimmer, M
• 通讯作者: Zimmer, M