Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/33230
Appears in Collections:Faculty of Social Sciences Book Chapters and Sections
Title: A more-than-human approach to researching AI at work: Alternative narratives for human and AI co-workers
Author(s): Thompson, Terrie Lynn
Graham, Bruce
Contact Email: terrielynn.thompson@stir.ac.uk
Editor(s): Bonderup Dohn, Nina
Jørgen Hansen, Jens
Børsen Hansen, Stig
Ryberg, Thomas
de Laat, Maarten
Citation: Thompson TL & Graham B (2021) A more-than-human approach to researching AI at work: Alternative narratives for human and AI co-workers. In: Bonderup Dohn N, Jørgen Hansen J, Børsen Hansen S, Ryberg T & de Laat M (eds.) Conceptualizing and innovating education and work with networked learning. Research in Networked Learning. Springer International Publishing. https://www.springer.com/gp/book/9783030852405
Keywords: artificial intelligence
networked learning
professional work
ethics of technology
more-than- human
public understanding of technology
Issue Date: 11-Nov-2021
Date Deposited: 6-Sep-2021
Series/Report no.: Research in Networked Learning
Abstract: Professional workers practice at the intersection of public narratives about artificial intelligence (AI), the AI industry, and regulatory frameworks. Yet, there is limited understanding of the interactions between workers, AI systems, and the publics they serve. To inform networked learning scholarship, there is a pressing need to study the knowledge that workers are developing as they learn to work with AI and the implications for networked learning within the workplace and higher education. We bring social and computing science perspectives alongside more-than-human sensitivities to explore how professional expertise, judgment, accountability, and control is being re-distributed between human workers and AI systems. By sketching the changes AI is provoking we highlight the fine-grained research and analysis necessary to ensure that AI design and deployment is critically informed by in-depth understandings of how people are actually engaging with algorithmic systems. We raise questions about what trust and confidence in new AI-infused work practices is needed (or possible). Attention is drawn to the complexities of AI-mediated work, which invites re-thinking ways to generate the evidence needed to inform networked work-learning practices. Highlighted throughout is the power of AI narratives and the importance of advancing alternative, more nuanced, narratives.
Rights: This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. This is a post-peer-review, pre-copyedit version of a paper published in Bonderup Dohn N, Jørgen Hansen J, Børsen Hansen S, Ryberg T & de Laat M (eds.) Conceptualizing and innovating education and work with networked learning. Research in Networked Learning. Springer International Publishing, 2021. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-85241-2
URL: https://www.springer.com/gp/book/9783030852405
Licence URL(s): https://storre.stir.ac.uk/STORREEndUserLicence.pdf

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