iSee: A case-based reasoning platform for the design of explanation experiences

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dc.contributor.authorCaro-Martínezen
dc.contributor.authorRecio-García, Juan A.en
dc.contributor.authorDíaz-Agudo, Belénen
dc.contributor.authorDarias, Jesus M.en
dc.contributor.authorWiratunga, Nirmalieen
dc.contributor.authorMartin, Kyleen
dc.contributor.authorWijekoon, Anjanaen
dc.contributor.authorNkisi-Orji, Ikechukwuen
dc.contributor.authorCorsar, Daviden
dc.contributor.authorPradeep, Preejaen
dc.contributor.authorBridge, Derek G.en
dc.contributor.authorLiret, Anneen
dc.contributor.funderCHIST-ERAen
dc.contributor.funderEngineering and Physical Sciences Research Councilen
dc.contributor.funderIrish Research Councilen
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderAgence Nationale de la Rechercheen
dc.contributor.funderMinisterio de Ciencia e Innovaciónen
dc.date.accessioned2024-09-03T14:52:44Z
dc.date.available2024-09-03T14:52:44Z
dc.date.issued2024-08-08en
dc.description.abstractExplainable Artificial Intelligence (XAI) is an emerging field within Artificial Intelligence (AI) that has provided many methods that enable humans to understand and interpret the outcomes of AI systems. However, deciding on the best explanation approach for a given AI problem is currently a challenging decision-making task. This paper presents the iSee project, which aims to address some of the XAI challenges by providing a unifying platform where personalized explanation experiences are generated using Case-Based Reasoning. An explanation experience includes the proposed solution to a particular explainability problem and its corresponding evaluation, provided by the end user. The ultimate goal is to provide an open catalog of explanation experiences that can be transferred to other scenarios where trustworthy AI is required.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid112305en
dc.identifier.citationCaro-Martínez, M., Recio-García, J.A., Díaz-Agudo, B., Darias, J.M., Wiratunga, N., Martin, K., Wijekoon, A., Nkisi-Orji, I., Corsar, D., Pradeep, P., Bridge, D. and Liret, A. (2024) ‘Isee: a case-based reasoning platform for the design of explanation experiences’, Knowledge-Based Systems, 302, 112305 (19 pp). Available at: https://doi.org/10.1016/j.knosys.2024.112305en
dc.identifier.doihttps://doi.org/10.1016/j.knosys.2024.112305en
dc.identifier.endpage19en
dc.identifier.issn0950-7051en
dc.identifier.journaltitleKnowledge-Based Systemsen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/16252
dc.identifier.volume302en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.ispartofKnowledge-Based Systemsen
dc.relation.projectinfo:eu-repo/grantAgreement/CHIST-ERA//CHIST-ERA-19-XAI-008/EU/Intelligent Sharing of Explanation Experience by Users for Users/iSeeen
dc.relation.projectinfo:eu-repo/grantAgreement/UKRI/EPSRC/EP/V061755/1/GB/iSee: Intelligent Sharing of Explanation Experience by Users for Users/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.rights© 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).en
dc.rights.urihttp://creativecommons.org/licenses/bync-nd/4.0/en
dc.subjecteXplainable artificial intelligenceen
dc.subjectTrustworthy AIen
dc.subjectCase-based reasoningen
dc.subjectArtificial Intelligence (AI)en
dc.titleiSee: A case-based reasoning platform for the design of explanation experiencesen
dc.typeArticle (peer-reviewed)en
oaire.citation.volume302en
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