Addressing Conceptual Gaps in Big Data Research Ethics: An Application of Contextual Integrity
Addressing Conceptual Gaps in Big Data Research Ethics: An Application of Contextual Integrity
复制标题
解决大数据研究伦理中的概念差距：情境完整性的应用
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
M. Zimmer
中科院分区：
文献类型：
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作者：
M. Zimmer
大数据的兴起为研究人员探索、观察和衡量人类的观点、活动和互动提供了新的途径。虽然学者、专业协会和伦理审查委员会已经建立了长期的研究伦理框架，以确保研究对象的权利和福利得到保护，但基于大数据的研究的迅速兴起对长期以来的伦理假设和准则提出了新的挑战。本文揭示了研究人员和伦理审查委员会在大数据研究背景下如何看待隐私，匿名，同意和伤害的新概念差距。最后引用尼森鲍姆的“隐私作为上下文完整性”理论作为指导大数据研究项目伦理决策的有益启发。
The rise of big data has provided new avenues for researchers to explore, observe, and measure human opinions, activities, and interactions. While scholars, professional societies, and ethical review boards have long-established research ethics frameworks to ensure the rights and welfare of the research subjects are protected, the rapid rise of big data-based research generates new challenges to long-held ethical assumptions and guidelines. This article discloses emerging conceptual gaps in relation to how researchers and ethical review boards think about privacy, anonymity, consent, and harm in the context of big data research. It closes by invoking Nissenbaum’s theory of “privacy as contextual integrity” as a useful heuristic to guide ethical decision-making in big data research projects.