关键数据伦理教学法:三种(非竞争性)方法

IF 6.5 1区 社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY Big Data & Society Pub Date : 2023-07-01 DOI:10.1177/20539517231203666
Luis Felipe R Murillo, Caitlin Wylie, Phil Bourne
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引用次数: 0

摘要

在一个关于数据密集型分析的伦理问题日益突出的时刻,“数据伦理”在教学、研究和实践中已经成为一个争论的焦点。在本文中,我们基于数据伦理学教学的经验,将这一争议置于语境中。我们描述了计算机伦理学领域如何在历史上为计算机专家的培训提供信息,以及近年来,科学和技术研究方面的奖学金如何创造机会,通过纳入关系伦理学和社会技术系统方面的批判性奖学金,来改变我们的教学方式。关于“关键数据伦理”的新兴文献为跨学科合作创造了一个空间,将技术和社会科学研究结合起来,通过实践方法检查数字系统的设计、实施和使用。作为对最近重塑和改变数据科学领域的努力的贡献,我们最后讨论了我们设计的方法,以弥合技术/社会鸿沟,并让学生在他们的数据实践中参与社会正义、问责制和开放性问题。
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Critical data ethics pedagogies: Three (non-rival) approaches
In a moment of heightened ethical questioning concerning data-intensive analytics, “data ethics” has become a site of dispute over its very definition in teaching, research, and practice. In this paper, we contextualize this dispute based on the experience of teaching data ethics. We describe how the field of computer ethics has historically informed the training of computer experts and how, in recent years, the scholarship on science and technology studies has created opportunities for transforming the way we teach with the inclusion of critical scholarship on relational ethics and sociotechnical systems. The emergent literature on “critical data ethics” has created a space for interdisciplinary collaboration that integrates technical and social science research to examine digital systems in their design, implementation, and use through a hands-on approach. As a contribution to the recent efforts to reimagine and transform the field of data science, we conclude with a discussion of the approach we devised to bridge technology/society divides and engage students with questions of social justice, accountability, and openness in their data practices.
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来源期刊
Big Data & Society
Big Data & Society SOCIAL SCIENCES, INTERDISCIPLINARY-
CiteScore
10.90
自引率
10.60%
发文量
59
审稿时长
11 weeks
期刊介绍: Big Data & Society (BD&S) is an open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities, and computing and their intersections with the arts and natural sciences. The journal focuses on the implications of Big Data for societies and aims to connect debates about Big Data practices and their effects on various sectors such as academia, social life, industry, business, and government. BD&S considers Big Data as an emerging field of practices, not solely defined by but generative of unique data qualities such as high volume, granularity, data linking, and mining. The journal pays attention to digital content generated both online and offline, encompassing social media, search engines, closed networks (e.g., commercial or government transactions), and open networks like digital archives, open government, and crowdsourced data. Rather than providing a fixed definition of Big Data, BD&S encourages interdisciplinary inquiries, debates, and studies on various topics and themes related to Big Data practices. BD&S seeks contributions that analyze Big Data practices, involve empirical engagements and experiments with innovative methods, and reflect on the consequences of these practices for the representation, realization, and governance of societies. As a digital-only journal, BD&S's platform can accommodate multimedia formats such as complex images, dynamic visualizations, videos, and audio content. The contents of the journal encompass peer-reviewed research articles, colloquia, bookcasts, think pieces, state-of-the-art methods, and work by early career researchers.
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