Social Biases in AI-Generated Creative Texts: A Mixed-Methods Approach in the Spanish Context

This study addresses the biases in artificial intelligence (AI) when generating creative content, a growing challenge due to the widespread adoption of these technologies in creating automated narratives. Biases in AI reflect and amplify social inequalities. They perpetuate stereotypes and limit div...

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Detalles Bibliográficos
Autores: Gabino-Campos, María, Baile, Jose I., Padilla-Martínez, Aura
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad a Distancia de Madrid (UDIMA)
Repositorio:udiMundus. Repositorio Institucional de la Universidad a Distancia de Madrid
OAI Identifier:oai:udimundus.udima.es:20.500.12226/2770
Acceso en línea:http://hdl.handle.net/20.500.12226/2770
Access Level:acceso abierto
Palabra clave:algorithmic biases
AI ethics
AI narrative analysis
gender stereotypes
age biases
ethnic biases
social representation
training datasets
physical appearance
socio-economic status
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oai_identifier_str oai:udimundus.udima.es:20.500.12226/2770
network_acronym_str ES
network_name_str España
repository_id_str
spelling Social Biases in AI-Generated Creative Texts: A Mixed-Methods Approach in the Spanish ContextGabino-Campos, MaríaBaile, Jose I.Padilla-Martínez, Auraalgorithmic biasesAI ethicsAI narrative analysisgender stereotypesage biasesethnic biasessocial representationtraining datasetsphysical appearancesocio-economic statusThis study addresses the biases in artificial intelligence (AI) when generating creative content, a growing challenge due to the widespread adoption of these technologies in creating automated narratives. Biases in AI reflect and amplify social inequalities. They perpetuate stereotypes and limit diverse representation in the generated outputs. Through an experimental approach with ChatGPT-4, biases related to age, gender, sexual orientation, ethnicity, religion, physical appearance, and socio-economic status, are analyzed in AI-generated stories about successful individuals in the context of Spain. The results reveal an overrepresentation of young, heterosexual, and Hispanic characters, alongside a marked underrepresentation of diverse groups such as older individuals, ethnic minorities, and characters with varied socio-economic backgrounds. These findings validate the hypothesis that AI systems replicate and amplify the biases present in their training data. This process reinforces social inequalities. To mitigate these effects, the study suggests solutions such as diversifying training datasets and conducting regular ethical audits, with the aim of fostering more inclusive AI systems. These measures seek to ensure that AI technologies fairly represent human diversity and contribute to a more equitable society.2024-25Departamento de Psicología y SaludFacultad de Psicología y Ciencias de la Salud(GI-14/1) Perspectiva psicológica en Trastornos del Comportamiento Alimentario y Obesidad2025info:eu-repo/semantics/articlehttp://hdl.handle.net/20.500.12226/2770reponame:udiMundus. Repositorio Institucional de la Universidad a Distancia de Madridinstname:Universidad a Distancia de Madrid (UDIMA)Inglésinfo:eu-repo/semantics/openAccessoai:udimundus.udima.es:20.500.12226/27702026-06-02T12:44:31Z
dc.title.none.fl_str_mv Social Biases in AI-Generated Creative Texts: A Mixed-Methods Approach in the Spanish Context
title Social Biases in AI-Generated Creative Texts: A Mixed-Methods Approach in the Spanish Context
spellingShingle Social Biases in AI-Generated Creative Texts: A Mixed-Methods Approach in the Spanish Context
Gabino-Campos, María
algorithmic biases
AI ethics
AI narrative analysis
gender stereotypes
age biases
ethnic biases
social representation
training datasets
physical appearance
socio-economic status
title_short Social Biases in AI-Generated Creative Texts: A Mixed-Methods Approach in the Spanish Context
title_full Social Biases in AI-Generated Creative Texts: A Mixed-Methods Approach in the Spanish Context
title_fullStr Social Biases in AI-Generated Creative Texts: A Mixed-Methods Approach in the Spanish Context
title_full_unstemmed Social Biases in AI-Generated Creative Texts: A Mixed-Methods Approach in the Spanish Context
title_sort Social Biases in AI-Generated Creative Texts: A Mixed-Methods Approach in the Spanish Context
dc.creator.none.fl_str_mv Gabino-Campos, María
Baile, Jose I.
Padilla-Martínez, Aura
author Gabino-Campos, María
author_facet Gabino-Campos, María
Baile, Jose I.
Padilla-Martínez, Aura
author_role author
author2 Baile, Jose I.
Padilla-Martínez, Aura
author2_role author
author
dc.subject.none.fl_str_mv algorithmic biases
AI ethics
AI narrative analysis
gender stereotypes
age biases
ethnic biases
social representation
training datasets
physical appearance
socio-economic status
topic algorithmic biases
AI ethics
AI narrative analysis
gender stereotypes
age biases
ethnic biases
social representation
training datasets
physical appearance
socio-economic status
description This study addresses the biases in artificial intelligence (AI) when generating creative content, a growing challenge due to the widespread adoption of these technologies in creating automated narratives. Biases in AI reflect and amplify social inequalities. They perpetuate stereotypes and limit diverse representation in the generated outputs. Through an experimental approach with ChatGPT-4, biases related to age, gender, sexual orientation, ethnicity, religion, physical appearance, and socio-economic status, are analyzed in AI-generated stories about successful individuals in the context of Spain. The results reveal an overrepresentation of young, heterosexual, and Hispanic characters, alongside a marked underrepresentation of diverse groups such as older individuals, ethnic minorities, and characters with varied socio-economic backgrounds. These findings validate the hypothesis that AI systems replicate and amplify the biases present in their training data. This process reinforces social inequalities. To mitigate these effects, the study suggests solutions such as diversifying training datasets and conducting regular ethical audits, with the aim of fostering more inclusive AI systems. These measures seek to ensure that AI technologies fairly represent human diversity and contribute to a more equitable society.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12226/2770
url http://hdl.handle.net/20.500.12226/2770
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Departamento de Psicología y Salud
Facultad de Psicología y Ciencias de la Salud
(GI-14/1) Perspectiva psicológica en Trastornos del Comportamiento Alimentario y Obesidad
publisher.none.fl_str_mv Departamento de Psicología y Salud
Facultad de Psicología y Ciencias de la Salud
(GI-14/1) Perspectiva psicológica en Trastornos del Comportamiento Alimentario y Obesidad
dc.source.none.fl_str_mv reponame:udiMundus. Repositorio Institucional de la Universidad a Distancia de Madrid
instname:Universidad a Distancia de Madrid (UDIMA)
instname_str Universidad a Distancia de Madrid (UDIMA)
reponame_str udiMundus. Repositorio Institucional de la Universidad a Distancia de Madrid
collection udiMundus. Repositorio Institucional de la Universidad a Distancia de Madrid
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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