Managing Collaborative Development of Artificial Intelligence: Lessons from the Field

dc.contributor.author Mayer, Anne-Sophie
dc.contributor.author Van Den Broek, Elmira
dc.contributor.author Kim, Bomi
dc.contributor.author Karacic, Tomislav
dc.contributor.author Sosa Hidalgo, Mario
dc.contributor.author Huysman, Marleen
dc.date.accessioned 2022-12-27T19:21:00Z
dc.date.available 2022-12-27T19:21:00Z
dc.date.issued 2023-01-03
dc.description.abstract Artificial intelligence (AI) promises businesses superior decisions that outperform those of domain experts. However, AI systems may fail on the ground when they are not developed in collaboration with the experts they seek to bypass. This raises the question of how to manage the collaborative development of AI. Building on a comparative field study, we reveal three key challenges of collaborative AI development in the area of consulting, hiring, and radiology. Based on these findings, we derive guidelines for managers that help them to facilitate the close engagement between AI developers and experts.
dc.format.extent 10
dc.identifier.doi 10.24251/HICSS.2023.744
dc.identifier.isbn 978-0-9981331-6-4
dc.identifier.uri https://hdl.handle.net/10125/103378
dc.language.iso eng
dc.relation.ispartof Proceedings of the 56th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Practice-based IS Research
dc.subject ai development
dc.subject collaboration
dc.subject comparative field study
dc.subject developers
dc.subject domain experts
dc.title Managing Collaborative Development of Artificial Intelligence: Lessons from the Field
dc.type.dcmi text
prism.startingpage 6139
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