Human Computer / Robot Interaction
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Paper Number
2460
Paper Type
Completed
Description
Contemporary research focuses on examining trustworthy AI but neglects to consider trust transfer processes, proposing that users’ established trust in a familiar source (e.g., a technology or person) may transfer to a novel target. We argue that such trust transfer processes also occur in the case of novel AI-capable technologies, as they are the result of the convergence of AI with one or more base technologies. We develop a model with a focus on multi-source trust transfer while including the theoretical framework of trustduality (i.e., trust in providers and trust in technologies) to advance our understanding about trust transfer. A survey among 432 participants confirms that users transfer their trust from known technologies and providers (i.e., vehicle and AI technology) to AI-capable technologies and their providers. The study contributes by providing a novel theoretical perspective on establishing trustworthy AI by validating the importance of the duality of trust.
Recommended Citation
Renner, Maximilian; Lins, Sebastian; Soellner, Matthias; Thiebes, Scott; and Sunyaev, Ali, "Achieving Trustworthy Artificial Intelligence: Multi-Source Trust Transfer in Artificial Intelligence-capable Technology" (2021). ICIS 2021 Proceedings. 15.
https://aisel.aisnet.org/icis2021/hci_robot/hci_robot/15
Achieving Trustworthy Artificial Intelligence: Multi-Source Trust Transfer in Artificial Intelligence-capable Technology
Contemporary research focuses on examining trustworthy AI but neglects to consider trust transfer processes, proposing that users’ established trust in a familiar source (e.g., a technology or person) may transfer to a novel target. We argue that such trust transfer processes also occur in the case of novel AI-capable technologies, as they are the result of the convergence of AI with one or more base technologies. We develop a model with a focus on multi-source trust transfer while including the theoretical framework of trustduality (i.e., trust in providers and trust in technologies) to advance our understanding about trust transfer. A survey among 432 participants confirms that users transfer their trust from known technologies and providers (i.e., vehicle and AI technology) to AI-capable technologies and their providers. The study contributes by providing a novel theoretical perspective on establishing trustworthy AI by validating the importance of the duality of trust.
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10-HCI