Trustworthy Conversational Agent Design for African Americans with Chronic Conditions during COVID-19
dc.contributor.author | JUNHAN, KIM | |
dc.contributor.author | MUHIC, JANA | |
dc.contributor.author | PARK, SUN YOUNG | |
dc.contributor.author | ROBERT, LIONEL | |
dc.date.accessioned | 2021-03-16T11:26:59Z | |
dc.date.available | 2021-03-16T11:26:59Z | |
dc.date.issued | 2021-03-16 | |
dc.identifier.citation | Kim, 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.uri | https://hdl.handle.net/2027.42/166481 | en |
dc.description.abstract | This 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.iso | en_US | en_US |
dc.publisher | CHI 2021 Workshop Realizing AI in Healthcare: Challenges Appearing in the Wild | en_US |
dc.subject | COVID-19 | en_US |
dc.subject | African Americans | en_US |
dc.subject | chatbots | en_US |
dc.subject | medical mistrust | en_US |
dc.subject | conversational agents | en_US |
dc.subject | trustworthy conversational agents | en_US |
dc.subject | health information | en_US |
dc.subject | long-held medical mistrust | en_US |
dc.title | Trustworthy Conversational Agent Design for African Americans with Chronic Conditions during COVID-19 | en_US |
dc.type | Conference Paper | en_US |
dc.subject.hlbsecondlevel | Information Science | |
dc.subject.hlbtoplevel | Social Sciences | |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Information, School of | en_US |
dc.contributor.affiliationum | Penny W. Stamps School Of Art & Design | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/166481/1/Kim et al. 2021.pdf | |
dc.identifier.doi | https://dx.doi.org/10.7302/277 | |
dc.identifier.source | Realizing AI in Healthcare: Challenges Appearing in the Wild at the 2021 CHI Conference on Human Factors in Computing Systems | en_US |
dc.identifier.orcid | 0000-0002-1410-2601 | en_US |
dc.description.filedescription | Description of Kim et al. 2021.pdf : Main Article | |
dc.description.depositor | SELF | en_US |
dc.identifier.name-orcid | Robert, Lionel P.; 0000-0002-1410-2601 | en_US |
dc.working.doi | 10.7302/277 | en_US |
dc.owningcollname | Information, School of (SI) |
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