iSee: intelligent sharing of explanation experiences

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dc.contributor.authorMartin, Kyleen
dc.contributor.authorWijekoon, Anjanaen
dc.contributor.authorWiratunga, Nirmalieen
dc.contributor.authorPalihawadana, Chamathen
dc.contributor.authorNkisi-Orji, Ikechukwuen
dc.contributor.authorCorsar, Daviden
dc.contributor.authorDíaz-Agudo, Belénen
dc.contributor.authorRecio-García, Juan A.en
dc.contributor.authorCaro-Martínez, Martaen
dc.contributor.authorBridge, Derek G.en
dc.contributor.authorPradeep, Preejaen
dc.contributor.authorLiret, Anneen
dc.contributor.authorFleisch, Brunoen
dc.contributor.funderEngineering and Physical Sciences Research Councilen
dc.date.accessioned2025-03-19T13:35:08Z
dc.date.available2025-03-19T13:35:08Z
dc.date.issued2022en
dc.description.abstractThe right to an explanation of the decision reached by a machine learning (ML) model is now an EU regulation. However, different system stakeholders may have different background knowledge, competencies and goals, thus requiring different kinds of explanations. There is a growing armoury of XAI methods, interpreting ML models and explaining their predictions, recommendations and diagnoses. We refer to these collectively as ”explanation strategies”. As these explanation strategies mature, practitioners gain experience in understanding which strategies to deploy in different circumstances. What is lacking, and what the iSee project will address, is the science and technology for capturing, sharing and re-using explanation strategies based on similar user experiences, along with a much-needed route to explainable AI (XAI) compliance. Our vision is to improve every user’s experience of AI, by harnessing experiences of best practice in XAI by providing an interactive environment where personalised explanation experiences are accessible to everyone. Video Link: https://youtu.be/81O6-q_yx0sen
dc.description.sponsorshipEngineering and Physical Sciences Research Council (Grant number EP/V061755/1)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMartin, K., Wijekoon, A., Wiratunga, N., Palihawadana, C., Nkisi-Orji, I., Corsar, D., Díaz-Agudo, B., Recio-García, J. A., Caro-Martínez, M., Bridge, D. and Pradeep, P. (2022) 'iSee: intelligent sharing of explanation experiences', in Reuss, P. and Schönborn, J. (eds.) Workshop proceedings of the 30th International conference on case-based reasoning (ICCBR-WS 2022), 12-15 September 2022, Nancy, France. CEUR Workshop Proceedings, 3389. Aachen: CEUR-WS [online], pp. 231-232. Available at: https://ceur-ws.org/Vol-3389/ICCBR_2022_Workshop_paper_83.pdfen
dc.identifier.endpage232en
dc.identifier.issn1613-0073en
dc.identifier.journaltitleCEUR Workshop Proceedingsen
dc.identifier.startpage231en
dc.identifier.urihttps://hdl.handle.net/10468/17188
dc.identifier.volume3389en
dc.language.isoenen
dc.publisherCEUR-WSen
dc.relation.ispartof30th International conference on case-based reasoning (ICCBR-WS 2022), 12-15 September 2022, Nancy, Franceen
dc.relation.urihttps://ceur-ws.org/Vol-3389/ICCBR_2022_Workshop_paper_83.pdfen
dc.rights© 2022, the Authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectExplainabilityen
dc.subjectCase-based reasoningen
dc.subjectProject showcaseen
dc.titleiSee: intelligent sharing of explanation experiencesen
dc.typeConference itemen
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