Publication:
Gender Bias, Citizen Participation, and AI

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dc.contributor.authorCuesta Leiva, Jose Antonio
dc.contributor.authorPecorari, Natalia Gisel
dc.date.accessioned2025-01-28T14:32:43Z
dc.date.available2025-01-28T14:32:43Z
dc.date.issued2025-01-28
dc.description.abstractThis paper investigates the role of gender bias in artificial intelligence–driven analyses of citizen participation, using data from the 2023 Latinobarómetro Survey. The paper proposes that gender bias—whether societal, data driven, or algorithmic—significantly affects civic engagement. Using machine learning, particularly decision trees, the analysis explores how self-reported societal bias (machismo norms) interacts with personal characteristics and circumstances to shape civic participation. The findings show that individuals with reportedly low levels of gender bias, who express political interest, have high levels of education, and align with left-wing views, are more likely to participate. The paper also explores different strategies to mitigate gender bias in both the data and the algorithms, demonstrating that gender bias remains a persistent factor even after applying corrective measures. Notably, lower machismo thresholds are required for participation in more egalitarian societies, with men needing to exhibit especially low machismo levels. Ultimately, the findings emphasize the importance of integrated strategies to tackle gender bias and increase participation, offering a framework for future studies to expand on nonlinear and complex social dynamics.en
dc.identifierhttp://documents.worldbank.org/curated/en/099909401272535729/IDU1758c3cc41f5be14ea519e4d16a2c1334c916
dc.identifier.doi10.1596/1813-9450-11046
dc.identifier.urihttps://hdl.handle.net/10986/42733
dc.languageEnglish
dc.language.isoen_US
dc.publisherWashington, DC: World Bank
dc.relation.ispartofseriesPolicy Research Working Paper; 11046
dc.rightsCC BY 3.0 IGO
dc.rights.holderWorld Bank
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/igo/
dc.subjectCITIZEN PARTICIPATION
dc.subjectGENDER BIAS
dc.subjectMACHINE LEARNING
dc.subjectLATIN AMERICA AND THE CARIBBEAN
dc.titleGender Bias, Citizen Participation, and AIen
dc.typeWorking Paper
dspace.entity.typePublication
okr.associatedcontenthttps://reproducibility.worldbank.org/index.php/catalog/233 Link to reproducibility package
okr.date.disclosure2025-01-28
okr.date.doiregistration2025-04-14T11:49:32.142563Z
okr.date.lastmodified2025-01-27T00:00:00Zen
okr.doctypePolicy Research Working Paper
okr.doctypePublications & Research
okr.docurlhttp://documents.worldbank.org/curated/en/099909401272535729/IDU1758c3cc41f5be14ea519e4d16a2c1334c916
okr.guid099909401272535729
okr.identifier.docmidIDU-758c3cc4-f5be-4ea5-9e4d-6a2c1334c916
okr.identifier.doi10.1596/1813-9450-11046
okr.identifier.externaldocumentum34450056
okr.identifier.internaldocumentum34450056
okr.identifier.reportWPS11046
okr.import.id6435
okr.importedtrueen
okr.language.supporteden
okr.pdfurlhttp://documents.worldbank.org/curated/en/099909401272535729/pdf/IDU1758c3cc41f5be14ea519e4d16a2c1334c916.pdfen
okr.region.geographicalCaribbean
okr.region.geographicalLatin America
okr.sectorFY17 - Public Administration - Social Protection
okr.topicMacroeconomics and Economic Growth::Economic Modeling and Statistics
okr.topicGender::Gender and Economics
okr.topicMacroeconomics and Economic Growth::Economics and Gender
okr.unitPlanet-Social Sustain&Inclus PM (SSIGL)
okr.unitPlanet-Social Sustain&Incl GD (SSIDR)
relation.isSeriesOfPublication26e071dc-b0bf-409c-b982-df2970295c87
relation.isSeriesOfPublication.latestForDiscovery26e071dc-b0bf-409c-b982-df2970295c87
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