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Trustworthy Conversational Agent Design for African Americans with Chronic Conditions during COVID-19

dc.contributor.authorJUNHAN, KIM
dc.contributor.authorMUHIC, JANA
dc.contributor.authorPARK, SUN YOUNG
dc.contributor.authorROBERT, LIONEL
dc.date.accessioned2021-03-16T11:26:59Z
dc.date.available2021-03-16T11:26:59Z
dc.date.issued2021-03-16
dc.identifier.citationKim, J., Muhic, J., Park, S., Robert, L. P. (2020). Trustworthy Conversational Agent Design for African Americans with Chronic Conditions during COVID-19 presented at the Realizing AI in Healthcare: Challenges Appearing in the Wild at the 2021 CHI Conference on Human Factors in Computing Systems (CHI 2021), May 8-13, 2021, Online Virtual Conference (originally Yokohama, Japan).en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/166481en
dc.description.abstractThis paper discusses preliminary findings on how to design chatbots that can increase African Americans’ trust in health information, particularly those who have experienced chronic conditions during the COVID-19 pandemic. COVID-19 has disproportionately affected the African American community in terms of severity and mortality, and scholars point towards the long-held medical mistrust among this population as a possible reason. Recent studies on the impact of conversational agents (CAs) on increasing trust in health information suggest that CAs can be effective. Through interviews and design studies with ten participants, we present four findings on how to design trustworthy CAs for our target population.en_US
dc.language.isoen_USen_US
dc.publisherCHI 2021 Workshop Realizing AI in Healthcare: Challenges Appearing in the Wilden_US
dc.subjectCOVID-19en_US
dc.subjectAfrican Americansen_US
dc.subjectchatbotsen_US
dc.subjectmedical mistrusten_US
dc.subjectconversational agentsen_US
dc.subjecttrustworthy conversational agentsen_US
dc.subjecthealth informationen_US
dc.subjectlong-held medical mistrusten_US
dc.titleTrustworthy Conversational Agent Design for African Americans with Chronic Conditions during COVID-19en_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.affiliationumPenny W. Stamps School Of Art & Designen_US
dc.contributor.affiliationumcampusAnn Arboren_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/166481/1/Kim et al. 2021.pdf
dc.identifier.doihttps://dx.doi.org/10.7302/277
dc.identifier.sourceRealizing AI in Healthcare: Challenges Appearing in the Wild at the 2021 CHI Conference on Human Factors in Computing Systemsen_US
dc.identifier.orcid0000-0002-1410-2601en_US
dc.description.filedescriptionDescription of Kim et al. 2021.pdf : Main Article
dc.description.depositorSELFen_US
dc.identifier.name-orcidRobert, Lionel P.; 0000-0002-1410-2601en_US
dc.working.doi10.7302/277en_US
dc.owningcollnameInformation, School of (SI)


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