The intersection of Evolutionary Computation and Explainable AI
dc.contributor.author | Bacardit, J | |
dc.contributor.author | Brownlee, AEI | |
dc.contributor.author | Cagnoni, S | |
dc.contributor.author | Iacca, G | |
dc.contributor.author | McCall, J | |
dc.contributor.author | Walker, David | |
dc.date.accessioned | 2022-07-14T08:31:58Z | |
dc.date.issued | 2022-07-09 | |
dc.identifier.isbn | 978-1-4503-9268-6 | |
dc.identifier.uri | http://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.extent | 1757-1762 | |
dc.language.iso | en | |
dc.publisher | ACM | |
dc.subject | Explainable Artificial Intelligence | |
dc.subject | Evolutionary Computation | |
dc.subject | Optimization | |
dc.subject | Machine Learning | |
dc.title | The intersection of Evolutionary Computation and Explainable AI | |
dc.type | conference | |
dc.type | Conference Proceeding | |
plymouth.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:001035469400276&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.conference-name | Genetic and Evolutionary Computation Conference (GECCO 2022) | |
plymouth.publication-status | Published | |
plymouth.journal | GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion | |
dc.identifier.doi | 10.1145/3520304.3533974 | |
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.dateAccepted | 2022-04-25 | |
dc.rights.embargodate | 2022-8-3 | |
rioxxterms.versionofrecord | 10.1145/3520304.3533974 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.licenseref.startdate | 2022-07-09 | |
rioxxterms.type | Conference Paper/Proceeding/Abstract |