Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical Perspective

Are algorithms sexist? This is a question that has been frequently appearing in the mass media, and the debate has typically been far from a scientific analysis. This paper aims at answering the question using a hybrid social and technical perspective. First a technical-oriented definition of the al...

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Detalles Bibliográficos
Autores: Castaneda, Juliana, Jover, Assumpta, Calvet Liñán, Laura, Yanes, Sergi, Juan, Angel A., Sainz, Milagros
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Universitat Oberta de Catalunya (UOC)
Repositorio:O2, repositorio institucional de la UOC
OAI Identifier:oai:openaccess.uoc.edu:10609/147827
Acceso en línea:http://hdl.handle.net/10609/147827
https://doi.org/10.3390/a15090303
Access Level:acceso abierto
Palabra clave:algorithmic bias
gender bias
data science
artificial intelligence
decision making
biaix algorítmic
biaix de gènere
ciència de dades
intel · ligència artificial
presa de decision
sesgo algorítmico
los prejuicios de género
ciencia de los datos
inteligencia artificial
toma de decisiones
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repository_id_str
spelling Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical PerspectiveCastaneda, JulianaJover, AssumptaCalvet Liñán, LauraYanes, SergiJuan, Angel A.Sainz, Milagrosalgorithmic biasgender biasdata scienceartificial intelligencedecision makingbiaix algorítmicbiaix de gènereciència de dadesintel · ligència artificialpresa de decisionsesgo algorítmicolos prejuicios de génerociencia de los datosinteligencia artificialtoma de decisionesAre algorithms sexist? This is a question that has been frequently appearing in the mass media, and the debate has typically been far from a scientific analysis. This paper aims at answering the question using a hybrid social and technical perspective. First a technical-oriented definition of the algorithm concept is provided, together with a more social-oriented interpretation. Secondly, several related works have been reviewed in order to clarify the state of the art in this matter, as well as to highlight the different perspectives under which the topic has been analyzed. Thirdly, we describe an illustrative numerical example possible discrimination in the banking sector due to data bias, and propose a simple but effective methodology to address it. Finally, a series of recommendations are provided with the goal of minimizing gender bias while designing and using data-algorithmic processes to support decision making in different environments.MDPIUniversitat Oberta de Catalunya. Estudis de Ciències de la Informació i de la ComunicacióUniversitat de ValènciaUniversitat Oberta de Catalunya. Gender and ICT (GenTIC)Universitat Politècnica de València202320232022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10609/147827https://doi.org/10.3390/a15090303reponame:O2, repositorio institucional de la UOCinstname:Universitat Oberta de Catalunya (UOC)InglésAlgorithms 2022, 15 (9)15;9https://www.mdpi.com/1999-4893/15/9/303CC BY SANOhttps://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:openaccess.uoc.edu:10609/1478272026-05-28T12:42:01Z
dc.title.none.fl_str_mv Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical Perspective
title Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical Perspective
spellingShingle Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical Perspective
Castaneda, Juliana
algorithmic bias
gender bias
data science
artificial intelligence
decision making
biaix algorítmic
biaix de gènere
ciència de dades
intel · ligència artificial
presa de decision
sesgo algorítmico
los prejuicios de género
ciencia de los datos
inteligencia artificial
toma de decisiones
title_short Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical Perspective
title_full Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical Perspective
title_fullStr Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical Perspective
title_full_unstemmed Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical Perspective
title_sort Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical Perspective
dc.creator.none.fl_str_mv Castaneda, Juliana
Jover, Assumpta
Calvet Liñán, Laura
Yanes, Sergi
Juan, Angel A.
Sainz, Milagros
author Castaneda, Juliana
author_facet Castaneda, Juliana
Jover, Assumpta
Calvet Liñán, Laura
Yanes, Sergi
Juan, Angel A.
Sainz, Milagros
author_role author
author2 Jover, Assumpta
Calvet Liñán, Laura
Yanes, Sergi
Juan, Angel A.
Sainz, Milagros
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Universitat Oberta de Catalunya. Estudis de Ciències de la Informació i de la Comunicació
Universitat de València
Universitat Oberta de Catalunya. Gender and ICT (GenTIC)
Universitat Politècnica de València
dc.subject.none.fl_str_mv algorithmic bias
gender bias
data science
artificial intelligence
decision making
biaix algorítmic
biaix de gènere
ciència de dades
intel · ligència artificial
presa de decision
sesgo algorítmico
los prejuicios de género
ciencia de los datos
inteligencia artificial
toma de decisiones
topic algorithmic bias
gender bias
data science
artificial intelligence
decision making
biaix algorítmic
biaix de gènere
ciència de dades
intel · ligència artificial
presa de decision
sesgo algorítmico
los prejuicios de género
ciencia de los datos
inteligencia artificial
toma de decisiones
description Are algorithms sexist? This is a question that has been frequently appearing in the mass media, and the debate has typically been far from a scientific analysis. This paper aims at answering the question using a hybrid social and technical perspective. First a technical-oriented definition of the algorithm concept is provided, together with a more social-oriented interpretation. Secondly, several related works have been reviewed in order to clarify the state of the art in this matter, as well as to highlight the different perspectives under which the topic has been analyzed. Thirdly, we describe an illustrative numerical example possible discrimination in the banking sector due to data bias, and propose a simple but effective methodology to address it. Finally, a series of recommendations are provided with the goal of minimizing gender bias while designing and using data-algorithmic processes to support decision making in different environments.
publishDate 2022
dc.date.none.fl_str_mv 2022
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10609/147827
https://doi.org/10.3390/a15090303
url http://hdl.handle.net/10609/147827
https://doi.org/10.3390/a15090303
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Algorithms 2022, 15 (9)
15;9
https://www.mdpi.com/1999-4893/15/9/303
dc.rights.none.fl_str_mv CC BY SA
NO
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv CC BY SA
NO
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:O2, repositorio institucional de la UOC
instname:Universitat Oberta de Catalunya (UOC)
instname_str Universitat Oberta de Catalunya (UOC)
reponame_str O2, repositorio institucional de la UOC
collection O2, repositorio institucional de la UOC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869402516369702912
score 15,300724

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