Leonelli Sabina
Department of Sociology, Philosophy and Anthropology, Exeter Centre for the Study of the Life Sciences, University of Exeter, Byrne House, St Germans Road, EX4 4PJ Exeter, UK
School of Humanities, University of Adelaide, Adelaide 5005, Australia.
Philos Trans A Math Phys Eng Sci. 2016 Dec 28;374(2083). doi: 10.1098/rsta.2016.0122.
The distributed and global nature of data science creates challenges for evaluating the quality, import and potential impact of the data and knowledge claims being produced. This has significant consequences for the management and oversight of responsibilities and accountabilities in data science. In particular, it makes it difficult to determine who is responsible for what output, and how such responsibilities relate to each other; what 'participation' means and which accountabilities it involves, with regard to data ownership, donation and sharing as well as data analysis, re-use and authorship; and whether the trust placed on automated tools for data mining and interpretation is warranted (especially as data processing strategies and tools are often developed separately from the situations of data use where ethical concerns typically emerge). To address these challenges, this paper advocates a participative, reflexive management of data practices. Regulatory structures should encourage data scientists to examine the historical lineages and ethical implications of their work at regular intervals. They should also foster awareness of the multitude of skills and perspectives involved in data science, highlighting how each perspective is partial and in need of confrontation with others. This approach has the potential to improve not only the ethical oversight for data science initiatives, but also the quality and reliability of research outputs.This article is part of the themed issue 'The ethical impact of data science'.
数据科学的分布式和全球性特征，给评估所产生的数据及知识主张的质量、重要性和潜在影响带来了挑战。这对数据科学中责任与问责的管理和监督有着重大影响。特别是，这使得难以确定谁对何种产出负责，以及这些责任如何相互关联；关于数据所有权、捐赠与共享以及数据分析、再利用和著作权，“参与”意味着什么，涉及哪些问责；以及对数据挖掘和解释的自动化工具的信任是否合理（尤其是因为数据处理策略和工具的开发往往与通常出现伦理问题的数据使用情况相分离）。为应对这些挑战，本文倡导对数据实践进行参与式、反思性管理。监管结构应鼓励数据科学家定期审视其工作的历史脉络和伦理影响。它们还应促进对数据科学中所涉及的众多技能和观点的认识，强调每种观点都是片面的，需要与其他观点相互碰撞。这种方法不仅有可能改善对数据科学计划的伦理监督，还能提高研究产出的质量和可靠性。本文是主题为“数据科学的伦理影响”的特刊的一部分。
