将FATE/关键数据研究整合到数据科学课程中:我们要去哪里?我们如何到达那里?

J. Bates, D. Cameron, Alessandro Checco, Paul D. Clough, F. Hopfgartner, Suvodeep Mazumdar, L. Sbaffi, Peter Stordy, Antonio de la Vega de León
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引用次数: 29

摘要

有很多人呼吁将与公平、问责、透明度、道德(FATE)和社会正义相关的主题整合到数据科学课程中,但很少有人探索如何在实践中发挥作用。本文介绍了由谢菲尔德大学(英国)信息学院的一个MSc数据科学教学团队参与的协作式自动人种学(CAE)的研究结果,其中FATE/关键数据研究(CDS)主题自2015/16年以来一直是课程的核心部分。在本文中,我们采用CAE方法来反思我们在学科交叉领域的工作经验,以及我们在将FATE/CDS整合到课程中的进展和未来计划。我们确定了一系列与我们自己的能力和英国高等教育更广泛的社会物质背景有关的更深层次的FATE/CDS整合的挑战。最后,我们为自己和更广泛的面向FATE/CDS的数据科学社区提出了建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating FATE/critical data studies into data science curricula: where are we going and how do we get there?
There have been multiple calls for integrating topics related to fairness, accountability, transparency, ethics (FATE) and social justice into Data Science curricula, but little exploration of how this might work in practice. This paper presents the findings of a collaborative auto-ethnography (CAE) engaged in by a MSc Data Science teaching team based at University of Sheffield (UK) Information School where FATE/Critical Data Studies (CDS) topics have been a core part of the curriculum since 2015/16. In this paper, we adopt the CAE approach to reflect on our experiences of working at the intersection of disciplines, and our progress and future plans for integrating FATE/CDS into the curriculum. We identify a series of challenges for deeper FATE/CDS integration related to our own competencies and the wider socio-material context of Higher Education in the UK. We conclude with recommendations for ourselves and the wider FATE/CDS orientated Data Science community.
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