Modeling implicit bias with fuzzy cognitive maps

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorTilburg University-
Autor(es): dc.contributorEindhoven University of Technology-
Autor(es): dc.contributorHasselt University-
Autor(es): dc.contributorCentral University of Las Villas-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorNápoles, Gonzalo-
Autor(es): dc.creatorGrau, Isel-
Autor(es): dc.creatorConcepción, Leonardo-
Autor(es): dc.creatorKoutsoviti Koumeri, Lisa-
Autor(es): dc.creatorPapa, João Paulo-
Data de aceite: dc.date.accessioned2025-08-21T19:06:51Z-
Data de disponibilização: dc.date.available2025-08-21T19:06:51Z-
Data de envio: dc.date.issued2022-05-01-
Data de envio: dc.date.issued2022-05-01-
Data de envio: dc.date.issued2022-04-07-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.neucom.2022.01.070-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/234080-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/234080-
Descrição: dc.descriptionThis paper presents a Fuzzy Cognitive Map model to quantify implicit bias in structured datasets where features can be numeric or discrete. In our proposal, problem features are mapped to neural concepts that are initially activated by experts when running what-if simulations, whereas weights connecting the neural concepts represent absolute correlation/association patterns between features. In addition, we introduce a new reasoning mechanism equipped with a normalization-like transfer function that prevents neurons from saturating. Another advantage of this new reasoning mechanism is that it can easily be controlled by regulating nonlinearity when updating neurons’ activation values in each iteration. Finally, we study the convergence of our model and derive analytical conditions concerning the existence and unicity of fixed-point attractors.-
Descrição: dc.descriptionDepartment of Cognitive Science & Artificial Intelligence Tilburg University-
Descrição: dc.descriptionInformation Systems Group Eindhoven University of Technology-
Descrição: dc.descriptionBusiness Informatics Research Group Hasselt University-
Descrição: dc.descriptionDepartment of Computer Science Central University of Las Villas-
Descrição: dc.descriptionDepartment of Computing São Paulo State University-
Descrição: dc.descriptionDepartment of Computing São Paulo State University-
Formato: dc.format33-45-
Idioma: dc.languageen-
Relação: dc.relationNeurocomputing-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectConvergence analysis-
Palavras-chave: dc.subjectFairness-
Palavras-chave: dc.subjectFuzzy cognitive maps-
Palavras-chave: dc.subjectImplicit bias-
Título: dc.titleModeling implicit bias with fuzzy cognitive maps-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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