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dc.contributor.authorCarrillo, Irenees-ES
dc.contributor.authorFernández, Césares-ES
dc.contributor.authorVicente, M. Asunciónes-ES
dc.contributor.authorGuilabert, Mercedeses-ES
dc.contributor.authorSánchez, Aliciaes-ES
dc.contributor.authorGil, Evaes-ES
dc.contributor.authorArroyo Rodríguez, Almudenaes-ES
dc.contributor.authorCalderón, Maríaes-ES
dc.contributor.authorCarratalá, M. Concepciónes-ES
dc.contributor.authorLópez López, Adrianaes-ES
dc.contributor.authorCoves Soler, Ángelaes-ES
dc.contributor.authorChilet, Elisaes-ES
dc.contributor.authorValero Verdu, Sergioes-ES
dc.contributor.authorSenabre, Carolinaes-ES
dc.date.accessioned2025-03-17T15:37:30Z
dc.date.available2025-03-17T15:37:30Z
dc.date.issued2024-11-10es_ES
dc.identifier.issn2783-7777es_ES
dc.identifier.urihttps://doi.org/10.33422/womensconf.v3i1.466es_ES
dc.descriptionArtículos en revistases_ES
dc.description.abstract.es-ES
dc.description.abstractCurrent Artificial Intelligence (AI) systems can effortlessly and instantaneously generate text, images, songs, and videos. This capability will lead us to a future where a significant portion of available information will be partially or wholly generated by AI. In this context, it is crucial to ensure that AI-generated texts and images do not perpetuate or exacerbate existing gender biases. We examined the behavior of two common AI chatbots, ChatGPT and Mistral, when generating text in Spanish, both in terms of language inclusiveness and perpetuation of traditional male/female roles. Our analysis revealed that both tools demonstrated relatively low gender bias in terms of reinforcing traditional gender roles but exhibited higher gender bias concerning language inclusiveness, at least in the Spanish language. Additionally, although ChatGPT showed lower overall gender bias than Mistral, Mistral provided users with more control to modify its behavior through prompt modifiers. As a final conclusion, while both AIs exhibit some degree of gender bias in their responses, this bias is significantly lower than the gender bias present in their human-authored source materials.en-GB
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoen-GBes_ES
dc.rightsCreative Commons Reconocimiento-NoComercial-SinObraDerivada Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/es_ES
dc.sourceRevista: Proceedings of The Global Conference on Women’s Studies, Periodo: 1, Volumen: 3, Número: 1, Página inicial: 29, Página final: 42es_ES
dc.titleDetecting and Reducing Gender Bias in Spanish Texts Generated with ChatGPT and Mistral Chatbots: The Lovelace Projectes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.description.versioninfo:eu-repo/semantics/publishedVersiones_ES
dc.rights.holderes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.keywords.es-ES
dc.keywordsartificial intelligence, gender bias, inclusive language, chatGPT, Mistralen-GB


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