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Disentangling Human-AI Hybrids: Conceptualizing the Interworking of Humans and AI-Enabled Systems

  • Artificial intelligence (AI) offers great potential in organizations. The path to achieving this potential will involve human-AI interworking, as has been confirmed by numerous studies. However, it remains to be explored which direction this interworking of human agents and AI-enabled systems ought to take. To date, research still lacks a holistic understanding of the entangled interworking that characterizes human-AI hybrids, so-called because they form when human agents and AI-enabled systems closely collaborate. To enhance such understanding, this paper presents a taxonomy of human-AI hybrids, developed by reviewing the current literature as well as a sample of 101 human-AI hybrids. Leveraging weak sociomateriality as justificatory knowledge, this study provides a deeper understanding of the entanglement between human agents and AI-enabled systems. Furthermore, a cluster analysis is performed to derive archetypes of human-AI hybrids, identifying ideal–typical occurrences of human-AI hybrids in practice. While the taxonomy creates aArtificial intelligence (AI) offers great potential in organizations. The path to achieving this potential will involve human-AI interworking, as has been confirmed by numerous studies. However, it remains to be explored which direction this interworking of human agents and AI-enabled systems ought to take. To date, research still lacks a holistic understanding of the entangled interworking that characterizes human-AI hybrids, so-called because they form when human agents and AI-enabled systems closely collaborate. To enhance such understanding, this paper presents a taxonomy of human-AI hybrids, developed by reviewing the current literature as well as a sample of 101 human-AI hybrids. Leveraging weak sociomateriality as justificatory knowledge, this study provides a deeper understanding of the entanglement between human agents and AI-enabled systems. Furthermore, a cluster analysis is performed to derive archetypes of human-AI hybrids, identifying ideal–typical occurrences of human-AI hybrids in practice. While the taxonomy creates a solid foundation for the understanding and analysis of human-AI hybrids, the archetypes illustrate the range of roles that AI-enabled systems can play in those interworking scenarios.show moreshow less

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Metadaten
Author:Lukas Fabri, Björn Häckel, Anna Maria Oberländer, Marius Rieg, Alexander Stohr
DOI:https://doi.org/10.1007/s12599-023-00810-1
Identifier:1867-0202 OPAC HS OPAC extern
Identifier:2363-7005 OPAC HS OPAC extern
Parent Title (English):Business & Information Systems Engineering
Publisher:Springer Gabler
Place of publication:Wiesbaden
Document Type:Article
Language:English
Date of Publication (online):2023/05/12
Year of first Publication:2023
Volume:2023
Number of pages:19 Seiten
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme
Open Access:open_access
Publication reviewed:begutachtet
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
Release Date:2023/07/28
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