%0 Conference Paper %1 rennermaximilian2021achieving %A Renner, Maximilian %A Lins, Sebastian %A Söllner, Matthias %A Thiebes, Scott %A Sunyaev, Ali %B International Conference on Information Systems (ICIS) %D 2021 %K itegpub pub_msö pub_wise-kassel %T Achieving Trustworthy Artificial Intelligence: Multi-Source Trust Transfer in Artificial Intelligence-capable Technology %X 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 trust duality (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.

@inproceedings{rennermaximilian2021achieving, 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 trust duality (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.}, added-at = {2021-10-05T12:35:25.000+0200}, author = {Renner, Maximilian and Lins, Sebastian and Söllner, Matthias and Thiebes, Scott and Sunyaev, Ali}, biburl = {https://www.bibsonomy.org/bibtex/255b0f1bf1884f42a307a132046fe24bb/wise-kassel}, booktitle = {International Conference on Information Systems (ICIS)}, eventdate = {12.12.2021 - 15.12.2021}, interhash = {366e3919c06fb92a978adf970e167deb}, intrahash = {55b0f1bf1884f42a307a132046fe24bb}, keywords = {itegpub pub_msö pub_wise-kassel}, timestamp = {2021-10-05T12:56:37.000+0200}, title = {Achieving Trustworthy Artificial Intelligence: Multi-Source Trust Transfer in Artificial Intelligence-capable Technology}, year = 2021 }