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

Renner, Maximilian; Lins, Sebastian; Söllner, Matthias; Thiebes, Scott ORCID iD icon; Sunyaev, Ali


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.

Preprint §
DOI: 10.5445/IR/1000138300
Veröffentlicht am 15.10.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Proceedingsbeitrag
Publikationsmonat/-jahr 12.2021
Sprache Englisch
Identifikator ISBN: 978-1-7336325-9-1
KITopen-ID: 1000138300
Erschienen in Building Sustainability and Resilience with IS: A Call fpr Action : ICIS 2021 proceedings ; 42nd International Conference on Information Systems (ICIS)
Veranstaltung 42nd International Conference on Information Systems (ICIS 2021), Austin, TX, USA, 12.12.2021 – 15.12.2021
Verlag AIS eLibrary (AISeL)
Externe Relationen Abstract/Volltext
Schlagwörter Trustworthy AI, Trust Transfer, Duality of Trust, AI-capable Technologies, Autonomous Vehicles, AI Convergence
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