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Causal Structure Learning of Bias for Fair Affect Recognition

cam.depositDate2022-12-02
dc.contributor.authorCheong, Jiaee
dc.contributor.authorKalkan, Sinan
dc.contributor.authorGunes, Hatice
dc.contributor.orcidGunes, Hatice [0000-0003-2407-3012]
dc.date.accessioned2022-12-06T00:30:36Z
dc.date.available2022-12-06T00:30:36Z
dc.date.updated2022-12-02T09:04:30Z
dc.description.abstractThe problem of bias in facial affect recognition tools can lead to severe consequences and issues. It has been posited that causality is able to address the gaps induced by the associational nature of traditional machine learning, and one such gap is that of fairness. However, given the nascency of the field, there is still no clear mapping between tools in causality and applications in fair machine learning for the specific task of affect recognition. To address this gap, we provide the first causal structure formalisation of the different biases that can arise in affect recognition. We conducted a proof of concept on utilising causal structure learning for the post-hoc understanding and analysing bias.
dc.description.sponsorshipAlan Turing Institute
dc.identifier.doi10.17863/CAM.91326
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/343902
dc.language.isoeng
dc.publisher.departmentDepartment of Computer Science And Technology
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleCausal Structure Learning of Bias for Fair Affect Recognition
dc.typeConference Object
dcterms.dateAccepted2022-11-14
pubs.conference-finish-date2023-01-07
pubs.conference-name2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2023) Workshops
pubs.conference-start-date2023-01-03
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/R030782/1)
pubs.funder-project-idAlan Turing Institute (ATIPO000004438)
pubs.funder-project-idAlan Turing Institute (Unknown)
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
pubs.licence-identifierapollo-deposit-licence-2-1
rioxxterms.versionAM

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