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dc.contributor.authorBacardit, J
dc.contributor.authorBrownlee, AEI
dc.contributor.authorCagnoni, S
dc.contributor.authorIacca, G
dc.contributor.authorMcCall, J
dc.contributor.authorWalker, D
dc.date.accessioned2022-07-14T08:31:58Z
dc.date.issued2022-07-09
dc.identifier.isbn978-1-4503-9268-6
dc.identifier.urihttp://hdl.handle.net/10026.1/19412
dc.description.abstract

In the past decade, Explainable Artificial Intelligence (XAI) has attracted a great interest in the research community, motivated by the need for explanations in critical AI applications. Some recent advances in XAI are based on Evolutionary Computation (EC) techniques, such as Genetic Programming. We call this trend EC for XAI. We argue that the full potential of EC methods has not been fully exploited yet in XAI, and call the community for future efforts in this field. Likewise, we find that there is a growing concern in EC regarding the explanation of population-based methods, i.e., their search process and outcomes. While some attempts have been done in this direction (although, in most cases, those are not explicitly put in the context of XAI), we believe that there are still several research opportunities and open research questions that, in principle, may promote a safer and broader adoption of EC in real-world applications. We call this trend XAI within EC. In this position paper, we briefly overview the main results in the two above trends, and suggest that the EC community may play a major role in the achievement of XAI.

dc.format.extent1757-1762
dc.language.isoen
dc.publisherACM
dc.subjectExplainable Artificial Intelligence
dc.subjectEvolutionary Computation
dc.subjectOptimization
dc.subjectMachine Learning
dc.titleThe intersection of Evolutionary Computation and Explainable AI
dc.typeconference
dc.typeProceedings Paper
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:001035469400276&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.publisher-urlhttp://dx.doi.org/10.1145/3520304.3533974
plymouth.conference-nameGenetic and Evolutionary Computation Conference (GECCO 2022)
plymouth.publication-statusPublished
plymouth.journalGECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion
dc.identifier.doi10.1145/3520304.3533974
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dcterms.dateAccepted2022-04-25
dc.rights.embargodate2022-8-3
rioxxterms.versionofrecord10.1145/3520304.3533974
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2022-07-09
rioxxterms.typeConference Paper/Proceeding/Abstract


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