Beitrag in einem Tagungsband
Achieving Trustworthy Artificial Intelligence: Multi-Source Trust Transfer in Artificial Intelligence-capable Technology
Details zur Publikation
Autor(inn)en: | Renner, M.; Lins, S.; Söllner, M.; Thiebes, S.; Sunyaev, A. |
Herausgeber: | Information Systems, Association |
Verlag: | AIS eLibrary (AISeL) |
Verlagsort / Veröffentlichungsort: | Austin, USA |
Publikationsjahr: | 2021 |
Seitenbereich: | TBD |
Buchtitel: | Building Sustainability and Resilience with IS: A Call for Action |
ISBN: | 978-1-7336325-9-1 |
Zusammenfassung, Abstract
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.
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.
