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From the Head or the Heart? An Experimental Design on the Impact of Explanation on Cognitive and Affective Trust

dc.contributor.authorZhang, Qiaoning
dc.contributor.authorYang, X. Jessie
dc.contributor.authorRobert, Lionel + "Jr"
dc.date.accessioned2021-10-08T11:26:57Z
dc.date.available2021-10-08T11:26:57Z
dc.date.issued2021-10-08
dc.identifier.citationZhang, Q., Yang, X. J. and Robert, L. P. (2021). From the Head or the Heart? The Impact of Explanation on Cognitive and Affective Trust, 2021 AAAI Fall Symposium on AI for HRI, Virtual, November 4-6, 2021en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/170376en
dc.description.abstractAutomated vehicles (AVs) are social robots that can potentially benefit our society. According to the existing literature, AV explanations can promote passengers’ trust by reducing the uncertainty associated with the AV’s reasoning and actions. However, the literature on AV explanations and trust has failed to consider how the type of trust—cognitive versus affective—might alter this relationship. Yet, the existing literature has shown that the implications associated with trust vary widely depending on whether it is cognitive or affective. To address this shortcoming and better understand the impacts of explanations on trust in AVs, we designed a study to investigate the effectiveness of explanations on both cognitive and affective trust. We expect these results to be of great significance in designing AV explanations to promote AV trust.en_US
dc.language.isoen_USen_US
dc.publisher2021 AAAI Fall Symposiumen_US
dc.subjectAutomated vehiclesen_US
dc.subjectautonomous vehiclesen_US
dc.subjectself driving carsen_US
dc.subjectvehicle trusten_US
dc.subjectautonomous vehicles trusten_US
dc.subjectautomated vehicles trusten_US
dc.subjectExplanationen_US
dc.subjectAI Explanationen_US
dc.subjectArtificial Intelligence Trusten_US
dc.subjectArtificial Intelligence Explanationen_US
dc.titleFrom the Head or the Heart? An Experimental Design on the Impact of Explanation on Cognitive and Affective Trusten_US
dc.typeConference Paperen_US
dc.subject.hlbsecondlevelInformation Science
dc.subject.hlbtoplevelSocial Sciences
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumInformation, School ofen_US
dc.contributor.affiliationumCollege of Engineeringen_US
dc.contributor.affiliationumcampusAnn Arboren_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/170376/1/Zhang et al. 2021 (Upload Fall 2021 AAAI).pdf
dc.identifier.doihttps://dx.doi.org/10.7302/3294
dc.identifier.source2021 AAAI Fall Symposiumen_US
dc.identifier.orcid0000-0002-1410-2601en_US
dc.description.filedescriptionDescription of Zhang et al. 2021 (Upload Fall 2021 AAAI).pdf : Article
dc.description.depositorSELFen_US
dc.identifier.name-orcidRobert, Lionel P.; 0000-0002-1410-2601en_US
dc.working.doi10.7302/3294en_US
dc.owningcollnameInformation, School of (SI)


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