A tool to overcome technical barriers for bias assessment in human language technologies
- Autores
- Alemany, Laura Alonso; Benotti, Luciana; Gonzalez, Lucía; Maina, Hernán Javier; Busaniche, Beatriz; Halvorsen, Alexia; Bordone, Matías; Sanchez, Jorge Adrian
- Año de publicación
- 2022
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Automatic processing of language is becoming pervasive in our lives, oftentaking central roles in our decision making, like choosing the wording for ourmessages and mails, translating our readings, or even having full conversationswith us. Word embeddings are a key component of modern natural languageprocessing systems. They provide a representation of words that has boosted theperformance of many applications, working as a semblance of meaning. Wordembeddings seem to capture a semblance of the meaning of words from raw text,but, at the same time, they also distill stereotypes and societal biases whichare subsequently relayed to the final applications. Such biases can bediscriminatory. It is very important to detect and mitigate those biases, toprevent discriminatory behaviors of automated processes, which can be much moreharmful than in the case of humans because their of their scale. There arecurrently many tools and techniques to detect and mitigate biases in wordembeddings, but they present many barriers for the engagement of people withouttechnical skills. As it happens, most of the experts in bias, either socialscientists or people with deep knowledge of the context where bias is harmful,do not have such skills, and they cannot engage in the processes of biasdetection because of the technical barriers. We have studied the barriers inexisting tools and have explored their possibilities and limitations withdifferent kinds of users. With this exploration, we propose to develop a toolthat is specially aimed to lower the technical barriers and provide theexploration power to address the requirements of experts, scientists and peoplein general who are willing to audit these technologies.
Fil: Alemany, Laura Alonso. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina
Fil: Benotti, Luciana. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Gonzalez, Lucía. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina
Fil: Maina, Hernán Javier. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina
Fil: Busaniche, Beatriz. Fundación Via Libre; Argentina
Fil: Halvorsen, Alexia. Fundación Via Libre; Argentina
Fil: Bordone, Matías. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina
Fil: Sanchez, Jorge Adrian. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina - Materia
-
Natural Language Processing
Language Models
Bias
Stereotypes and Discrimination - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/211790
Ver los metadatos del registro completo
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A tool to overcome technical barriers for bias assessment in human language technologiesAlemany, Laura AlonsoBenotti, LucianaGonzalez, LucíaMaina, Hernán JavierBusaniche, BeatrizHalvorsen, AlexiaBordone, MatíasSanchez, Jorge AdrianNatural Language ProcessingLanguage ModelsBiasStereotypes and Discriminationhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Automatic processing of language is becoming pervasive in our lives, oftentaking central roles in our decision making, like choosing the wording for ourmessages and mails, translating our readings, or even having full conversationswith us. Word embeddings are a key component of modern natural languageprocessing systems. They provide a representation of words that has boosted theperformance of many applications, working as a semblance of meaning. Wordembeddings seem to capture a semblance of the meaning of words from raw text,but, at the same time, they also distill stereotypes and societal biases whichare subsequently relayed to the final applications. Such biases can bediscriminatory. It is very important to detect and mitigate those biases, toprevent discriminatory behaviors of automated processes, which can be much moreharmful than in the case of humans because their of their scale. There arecurrently many tools and techniques to detect and mitigate biases in wordembeddings, but they present many barriers for the engagement of people withouttechnical skills. As it happens, most of the experts in bias, either socialscientists or people with deep knowledge of the context where bias is harmful,do not have such skills, and they cannot engage in the processes of biasdetection because of the technical barriers. We have studied the barriers inexisting tools and have explored their possibilities and limitations withdifferent kinds of users. With this exploration, we propose to develop a toolthat is specially aimed to lower the technical barriers and provide theexploration power to address the requirements of experts, scientists and peoplein general who are willing to audit these technologies.Fil: Alemany, Laura Alonso. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; ArgentinaFil: Benotti, Luciana. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gonzalez, Lucía. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; ArgentinaFil: Maina, Hernán Javier. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; ArgentinaFil: Busaniche, Beatriz. Fundación Via Libre; ArgentinaFil: Halvorsen, Alexia. Fundación Via Libre; ArgentinaFil: Bordone, Matías. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; ArgentinaFil: Sanchez, Jorge Adrian. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaCornell University2022-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/211790Alemany, Laura Alonso; Benotti, Luciana; Gonzalez, Lucía; Maina, Hernán Javier; Busaniche, Beatriz; et al.; A tool to overcome technical barriers for bias assessment in human language technologies; Cornell University; arXiv; 2; 7-2022; 1-192331-8422CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/2207.06591v2info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:38:10Zoai:ri.conicet.gov.ar:11336/211790instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:38:10.681CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
A tool to overcome technical barriers for bias assessment in human language technologies |
title |
A tool to overcome technical barriers for bias assessment in human language technologies |
spellingShingle |
A tool to overcome technical barriers for bias assessment in human language technologies Alemany, Laura Alonso Natural Language Processing Language Models Bias Stereotypes and Discrimination |
title_short |
A tool to overcome technical barriers for bias assessment in human language technologies |
title_full |
A tool to overcome technical barriers for bias assessment in human language technologies |
title_fullStr |
A tool to overcome technical barriers for bias assessment in human language technologies |
title_full_unstemmed |
A tool to overcome technical barriers for bias assessment in human language technologies |
title_sort |
A tool to overcome technical barriers for bias assessment in human language technologies |
dc.creator.none.fl_str_mv |
Alemany, Laura Alonso Benotti, Luciana Gonzalez, Lucía Maina, Hernán Javier Busaniche, Beatriz Halvorsen, Alexia Bordone, Matías Sanchez, Jorge Adrian |
author |
Alemany, Laura Alonso |
author_facet |
Alemany, Laura Alonso Benotti, Luciana Gonzalez, Lucía Maina, Hernán Javier Busaniche, Beatriz Halvorsen, Alexia Bordone, Matías Sanchez, Jorge Adrian |
author_role |
author |
author2 |
Benotti, Luciana Gonzalez, Lucía Maina, Hernán Javier Busaniche, Beatriz Halvorsen, Alexia Bordone, Matías Sanchez, Jorge Adrian |
author2_role |
author author author author author author author |
dc.subject.none.fl_str_mv |
Natural Language Processing Language Models Bias Stereotypes and Discrimination |
topic |
Natural Language Processing Language Models Bias Stereotypes and Discrimination |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
Automatic processing of language is becoming pervasive in our lives, oftentaking central roles in our decision making, like choosing the wording for ourmessages and mails, translating our readings, or even having full conversationswith us. Word embeddings are a key component of modern natural languageprocessing systems. They provide a representation of words that has boosted theperformance of many applications, working as a semblance of meaning. Wordembeddings seem to capture a semblance of the meaning of words from raw text,but, at the same time, they also distill stereotypes and societal biases whichare subsequently relayed to the final applications. Such biases can bediscriminatory. It is very important to detect and mitigate those biases, toprevent discriminatory behaviors of automated processes, which can be much moreharmful than in the case of humans because their of their scale. There arecurrently many tools and techniques to detect and mitigate biases in wordembeddings, but they present many barriers for the engagement of people withouttechnical skills. As it happens, most of the experts in bias, either socialscientists or people with deep knowledge of the context where bias is harmful,do not have such skills, and they cannot engage in the processes of biasdetection because of the technical barriers. We have studied the barriers inexisting tools and have explored their possibilities and limitations withdifferent kinds of users. With this exploration, we propose to develop a toolthat is specially aimed to lower the technical barriers and provide theexploration power to address the requirements of experts, scientists and peoplein general who are willing to audit these technologies. Fil: Alemany, Laura Alonso. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina Fil: Benotti, Luciana. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Gonzalez, Lucía. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina Fil: Maina, Hernán Javier. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina Fil: Busaniche, Beatriz. Fundación Via Libre; Argentina Fil: Halvorsen, Alexia. Fundación Via Libre; Argentina Fil: Bordone, Matías. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina Fil: Sanchez, Jorge Adrian. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina |
description |
Automatic processing of language is becoming pervasive in our lives, oftentaking central roles in our decision making, like choosing the wording for ourmessages and mails, translating our readings, or even having full conversationswith us. Word embeddings are a key component of modern natural languageprocessing systems. They provide a representation of words that has boosted theperformance of many applications, working as a semblance of meaning. Wordembeddings seem to capture a semblance of the meaning of words from raw text,but, at the same time, they also distill stereotypes and societal biases whichare subsequently relayed to the final applications. Such biases can bediscriminatory. It is very important to detect and mitigate those biases, toprevent discriminatory behaviors of automated processes, which can be much moreharmful than in the case of humans because their of their scale. There arecurrently many tools and techniques to detect and mitigate biases in wordembeddings, but they present many barriers for the engagement of people withouttechnical skills. As it happens, most of the experts in bias, either socialscientists or people with deep knowledge of the context where bias is harmful,do not have such skills, and they cannot engage in the processes of biasdetection because of the technical barriers. We have studied the barriers inexisting tools and have explored their possibilities and limitations withdifferent kinds of users. With this exploration, we propose to develop a toolthat is specially aimed to lower the technical barriers and provide theexploration power to address the requirements of experts, scientists and peoplein general who are willing to audit these technologies. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-07 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/211790 Alemany, Laura Alonso; Benotti, Luciana; Gonzalez, Lucía; Maina, Hernán Javier; Busaniche, Beatriz; et al.; A tool to overcome technical barriers for bias assessment in human language technologies; Cornell University; arXiv; 2; 7-2022; 1-19 2331-8422 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/211790 |
identifier_str_mv |
Alemany, Laura Alonso; Benotti, Luciana; Gonzalez, Lucía; Maina, Hernán Javier; Busaniche, Beatriz; et al.; A tool to overcome technical barriers for bias assessment in human language technologies; Cornell University; arXiv; 2; 7-2022; 1-19 2331-8422 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/2207.06591v2 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Cornell University |
publisher.none.fl_str_mv |
Cornell University |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
reponame_str |
CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.name.fl_str_mv |
CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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1844613206319824896 |
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13.070432 |