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Expectation management in AI

2024-04-15Artigo científico Acesso aberto
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dc.contributor.authorKinney, Marjorie
dc.contributor.authorAnastasiadou, Maria
dc.contributor.authorNaranjo-Zolotov, Mijail
dc.contributor.authorSantos, Vítor
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblElsevier
dc.date.accessioned2024-04-04T00:02:50Z
dc.date.available2024-04-04T00:02:50Z
dc.date.issued2024-04-15
dc.descriptionKinney, M., Anastasiadou, M., Naranjo-Zolotov, M., & Santos, V. (2024). Expectation management in AI: A framework for understanding stakeholder trust and acceptance of artificial intelligence systems. Heliyon, 10(7), 1-22. Article e28562. https://doi.org/10.1016/j.heliyon.2024.e28562 --- This work was supported by funds of Fundação para a Ciência e a Tecnologia (FCT) through a research grant to the Information Management Research Center – MagIC/NOVA IMS (UIDB/04152/2020)
dc.description.abstractAs artificial intelligence systems gain traction, their trustworthiness becomes paramount to harness their benefits and mitigate risks. This study underscores the pressing need for an expectation management framework to align stakeholder anticipations before any system-related activities, such as data collection, modeling, or implementation. To this end, we introduce a comprehensive framework tailored to capture end-user expectations specifically for trustworthy artificial intelligence systems. To ensure its relevance and robustness, we validated the framework via semi-structured interviews, encompassing questions rooted in the framework's constructs and principles. These interviews engaged fourteen diverse end users across the healthcare and education sectors, including physicians, teachers, and students. Through a meticulous qualitative analysis of the interview transcripts, we unearthed pivotal themes and discerned varying perspectives among the interviewee groups. Ultimately, our framework stands as a pivotal tool, paving the way for in-depth discussions about user expectations, illuminating the significance of various system attributes, and spotlighting potential challenges that might jeopardize the system's efficacy.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent22
dc.format.extent1516035
dc.identifier.doi10.1016/j.heliyon.2024.e28562
dc.identifier.issn2405-8440
dc.identifier.otherPURE: 87134057
dc.identifier.otherPURE UUID: 9fc96861-bedc-4f85-87c2-73630341e61b
dc.identifier.othercrossref: 10.1016/j.heliyon.2024.e28562
dc.identifier.otherScopus: 85189142979
dc.identifier.otherWOS: 001217686800001
dc.identifier.otherORCID: /0000-0001-5153-3315/work/156929774
dc.identifier.otherORCID: /0000-0002-4223-7079/work/156930311
dc.identifier.otherORCID: /0000-0003-2770-7025/work/215175649
dc.identifier.urihttp://hdl.handle.net/10362/165787
dc.identifier.urlhttps://www.scopus.com/pages/publications/85189142979
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001217686800001
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
dc.relationInformation Management Research Center
dc.subjectExpectation management
dc.subjectArtificial intelligence
dc.subjectMachine learning
dc.subjectTrustworthy AI
dc.subjectExplainable AI
dc.subjectAI development process
dc.subjectAI in education
dc.subjectAI in health
dc.subjectGeneral
dc.subjectSDG 4 - Quality Education
dc.subjectSDG 9 - Industry, Innovation, and Infrastructure
dc.titleExpectation management in AIen
dc.title.subtitleA framework for understanding stakeholder trust and acceptance of artificial intelligence systemsen
dc.typejournal article
degois.publication.firstPage1
degois.publication.issue7
degois.publication.lastPage22
degois.publication.titleHeliyon
degois.publication.volume10
dspace.entity.typePublication
oaire.awardNumberUIDB/04152/2020
oaire.awardTitleInformation Management Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication3274bdb3-4dd3-4bbe-8f74-d34190081f87
relation.isProjectOfPublication.latestForDiscovery3274bdb3-4dd3-4bbe-8f74-d34190081f87

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NIMS: MagIC - Artigos em revista internacional com arbitragem científica (Peer-Review articles in international journals)