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dc.contributor.authorMartin, Kyle
dc.contributor.authorLiret, Anne
dc.contributor.authorWiratunga, Nirmalie
dc.contributor.authorOwusu, Gilbert
dc.contributor.authorKern, Mathias
dc.date.accessioned2019-02-04T15:50:15Z
dc.date.available2019-02-04T15:50:15Z
dc.date.issued2018-07-09en
dc.identifier.citationMARTIN, K., LIRET, A., WIRATUNGA, N., OWUSU, G. and KERN, M. 2018. Explainability through transparency and user control: a case-based recommender for engineering workers. In Minor, M. (ed.) Workshop proceedings of the 26th International conference on case-based reasoning (ICCBR 2018), 9-12 July 2018, Stockholm, Sweden. Stockholm: ICCBR [online], pages 22-31. Available from: http://iccbr18.com/wp-content/uploads/ICCBR-2018-V3.pdf#page=22en
dc.identifier.urihttp://hdl.handle.net/10059/3278
dc.description.abstractWithin the service providing industries, field engineers can struggle to access tasks which are suited to their individual skills and experience. There is potential for a recommender system to improve access to information while being on site. However the smooth adoption of such a system is superseded by a challenge for exposing the human understandable proof of the machine reasoning.With that in mind, this paper introduces an explainable recommender system to facilitate transparent retrieval of task information for field engineers in the context of service delivery. The presented software adheres to the five goals of an explainable intelligent system and incorporates elements of both Case-Based Reasoning and heuristic techniques to develop a recommendation ranking of tasks. In addition we evaluate methods of building justifiable representations for similarity-based return on a classification task developed from engineers' notes. Our conclusion highlights the trade-off between performance and explainability.en
dc.language.isoengen
dc.publisherICCBR (ORGANISERS)en
dc.rightshttps://creativecommons.org/licenses/by-nc/4.0en
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectCase based reasoningen
dc.subjectRecommender systemsen
dc.subjectExplainable AIen
dc.subjectInformation retrievalen
dc.subjectMachine learningen
dc.titleExplainability through transparency and user control: a case-based recommender for engineering workers.en
dc.typeConference publicationsen
dc.publisher.urihttp://iccbr18.com/wp-content/uploads/ICCBR-2018-V3.pdf#page=22en
dcterms.publicationdate2018-07-12en
refterms.accessExceptionNAen
refterms.depositExceptionNAen
refterms.panelBen
refterms.technicalExceptionNAen
refterms.versionAMen
rioxxterms.publicationdate2018-07-09en
rioxxterms.typeConference Paper/Proceeding/Abstracten
rioxxterms.versionAMen


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