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Paper Type
Complete
Description
This literature review examines trust in AI-augmented decision-making systems in collaborative settings. The analysis suggests that human characteristics, AI characteristics, the decision-making environment, and the collaboration setting are antecedents of human trust in AI. This literature review contributes to the theoretical knowledge of trust with a dynamic learned trust framework, which represents an integrated understanding of trust in AI in human-AI collaborative decision-making. The review also provides practical implications for AI designers and implementors for improving the trustworthiness and adoption of AI for human-AI collaboration. By providing insight for researchers and audiences in information systems, organization and management, this paper can serve as a starting point for investigating the nuances, complexities and dynamics of trust in human-AI relationships.
Paper Number
1507
Recommended Citation
Baduna, Maryam and Hylving, Lena, "Dynamics of Trust: Unpacking Trust in Human-AI Collaboration in Decision-Making" (2023). AMCIS 2023 Proceedings. 11.
https://aisel.aisnet.org/amcis2023/sig_aiaa/sig_aiaa/11
Dynamics of Trust: Unpacking Trust in Human-AI Collaboration in Decision-Making
This literature review examines trust in AI-augmented decision-making systems in collaborative settings. The analysis suggests that human characteristics, AI characteristics, the decision-making environment, and the collaboration setting are antecedents of human trust in AI. This literature review contributes to the theoretical knowledge of trust with a dynamic learned trust framework, which represents an integrated understanding of trust in AI in human-AI collaborative decision-making. The review also provides practical implications for AI designers and implementors for improving the trustworthiness and adoption of AI for human-AI collaboration. By providing insight for researchers and audiences in information systems, organization and management, this paper can serve as a starting point for investigating the nuances, complexities and dynamics of trust in human-AI relationships.
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