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

Title data

Fabri, Lukas ; Häckel, Björn ; Oberländer, Anna Maria ; Rieg, Marius ; Stohr, Alexander:
Disentangling Human-AI Hybrids : Conceptualizing the Interworking of Humans and AI-enabled Systems.
In: Business & Information Systems Engineering. Vol. 65 (2023) . - pp. 623-641.
ISSN 1867-0202
DOI: https://doi.org/10.1007/s12599-023-00810-1

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Abstract in another language

Artificial intelligence (AI) offers enormous potential in organizations. The path to achieving this potential will involve human-AI collaboration, as has been confirmed by numerous studies. However, it remains to be explored which direction this collaboration of human agents and AI-enabled systems ought to take. To date, there has been little research and no holistic understanding of the entangled interworking that characterizes human-AI hybrids, so-called because they form when human agents and AI-enabled systems 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 hybrid use cases. Leveraging weak sociomateriality as theoretical lens, 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 collaborative interworking scenarios.