Understanding Cognition in the Development of Artificial Intelligence-based Systems: An Exploration of Cognitive Fit and Supporting Mechanisms

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dc.contributor.authorWu-Gehbauer, Mei
dc.contributor.authorRosenkranz, Christoph
dc.contributor.authorHennel, Phil
dc.date.accessioned2023-12-26T18:36:26Z
dc.date.available2023-12-26T18:36:26Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2024.097
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.other848e2c4b-df15-4041-93be-251e5d8c0258
dc.identifier.urihttps://hdl.handle.net/10125/106474
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectAI-based Decision Support for Organizational Processes
dc.subjectartificial intelligence
dc.subjectcognitive fit
dc.subjectdata science team
dc.subjectsupporting mechanisms
dc.titleUnderstanding Cognition in the Development of Artificial Intelligence-based Systems: An Exploration of Cognitive Fit and Supporting Mechanisms
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractArtificial intelligence (AI) increasingly supports users in their work practices in organizations. To unlock value from AI, users need to interact with AI-based systems in their decision-making processes. With increasing sophistication of AI, understanding the mechanisms influencing the cognitive fit between users and AI-based systems is crucial to ensure a successful implementation. Drawing upon literature on cognition in IS and employing a revelatory case study, we explore mechanisms enacted by data science teams in shaping a cognitive fit. We find that the data science teams enact supporting mechanisms to enable users to make sense of AI-based systems and AI “making sense” of users mental models. With this, we contribute to the body of knowledge on cognitive fit in IS by shedding light on underlying mechanisms for an effective development of AI-based systems. For organizations, these insights are valuable to overcome barriers to the successful introduction of AI-based systems.
dcterms.extent10 pages
prism.startingpage800

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