Proceedings3057

Achieving Trustworthy Artificial Intelligence: Multi-Source Trust Transfer in Artificial Intelligence-capable Technology

Maximilian Renner, Sebastian Lins, Matthias Söllner, Scott Thiebes, Ali Sunyaev

Published: 2021 Dezember
Nummer: 42
Verlag: Association for Information Systems
Erscheinungsort: Austin, TX, USA
Organisation: 42nd International Conference on Information Systems (ICIS)
BibTeX

Kurzfassung
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.

Download: Media:ICIS2021_TrustworthyAI.pdf

Forschungsgruppe

Critical Information Infrastructures

Forschungsgebiet

Trustworthy AI