Bates, J. orcid.org/0000-0001-7266-8470, Cameron, D., Checco, A. orcid.org/0000-0002-0981-3409 et al. (6 more authors) (2020) Integrating FATE/critical data studies into data science curricula : where are we going and how do we get there? In: Hildebrandt, M., Castillo, C., Celis, E., Ruggieri, S., Taylor, L. and Zanfir-Fortuna, G., (eds.) FAT* '20: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. FAT* '20: Conference on Fairness, Accountability, and Transparency, 27-30 Jan 2020, Barcelona, Spain. Association for Computing Machinery (ACM) , pp. 425-435. ISBN 9781450369367
Abstract
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2020 Association for Computing Machinery. This is an author-produced version of a paper subsequently published in FAT* '20: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 26 Jun 2020 07:34 |
Last Modified: | 10 Jun 2021 09:55 |
Status: | Published |
Publisher: | Association for Computing Machinery (ACM) |
Refereed: | Yes |
Identification Number: | 10.1145/3351095.3372832 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:162447 |