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

该项目通过提供推动公平、公正的计算研究所需的经验知识，促进科学技术发展的进步。关于人的大量、无所不在的数据从根本上推动了新的计算研究，但也带来了新的伦理挑战，比如大规模地计算分布式危害，防范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。在线实验、泄露的数据集以及“公共”数据的定义引发了全国性的争论。调查人员很难就如何吸引弱势群体或指导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进行辩论。由于缺乏对新兴规范和期望的经验知识，解决这些挑战的研究遇到了障碍。这个项目发现了不同的利益相关者——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，以及他们的决定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无处不在的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伦理性的基本问题。了解计算研究人员如何在大数据时代适应他们的实践，并强调与数据现实、用户期望和监管实践的融合或冲突点，将为普遍存在的数据伦理提供具体指导。除了改进在计算机环境中研究人类的伦理方法外，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。产出，如决策支持工具、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，旨在支持广泛的数据伦理利益相关者。为了实现这些目标，该项目建立了一个协作实验室——一个结合数据和分析资源的虚拟中心——以收集不同范围和规模的研究伦理经验数据。这项研究包括对多个伦理问题（隐私、风险、尊重、慈善、正义）的关注，以及涉及研究伦理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）。该项目对以下方面进行了采访和调查：1)用户社区，2)计算研究人员，3)数据伦理监管机构，以及4)商业平台提供商。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)普适数据研究的新闻文章和公共话语，4)现有数据伦理培训语料，5)普适数据授权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：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受试者参与无处不在的数据研究的意愿；以计算研究人员可访问的方式对用户关注点进行建模；发现现有的道德准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伦理最佳实践。

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.