An analysis of unconscious gender bias in academic texts by means of a decision algorithm.

Inclusive language focuses on using the vocabulary to avoid exclusion or discrimination, specially referred to gender. The task of finding gender bias in written documents must be performed manually, and it is a time-consuming process. Consequently, studying the usage of non-inclusive language on a...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Pedro Orgeira-Crespo, Carla Míguez-Álvarez, Miguel Cuevas-Alonso, Elena Rivo-López
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ac933f33d8a74ac2bf1204b25abfb9ca
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:ac933f33d8a74ac2bf1204b25abfb9ca
record_format	dspace
spelling	oai:doaj.org-article:ac933f33d8a74ac2bf1204b25abfb9ca2021-12-02T20:13:54ZAn analysis of unconscious gender bias in academic texts by means of a decision algorithm.1932-620310.1371/journal.pone.0257903https://doaj.org/article/ac933f33d8a74ac2bf1204b25abfb9ca2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0257903https://doaj.org/toc/1932-6203Inclusive language focuses on using the vocabulary to avoid exclusion or discrimination, specially referred to gender. The task of finding gender bias in written documents must be performed manually, and it is a time-consuming process. Consequently, studying the usage of non-inclusive language on a document, and the impact of different document properties (such as author gender, date of presentation, etc.) on how many non-inclusive instances are found, is quite difficult or even impossible for big datasets. This research analyzes the gender bias in academic texts by analyzing a study corpus of more than 12,000 million words obtained from more than one hundred thousand doctoral theses from Spanish universities. For this purpose, an automated algorithm was developed to evaluate the different characteristics of the document and look for interactions between age, year of publication, gender or the field of knowledge in which the doctoral thesis is framed. The algorithm identified information patterns using a CNN (convolutional neural network) by the creation of a vector representation of the sentences. The results showed evidence that there was a greater bias as the age of the authors increased, who were more likely to use non-inclusive terms; it was concluded that there is a greater awareness of inclusiveness in women than in men, and also that this awareness grows as the candidate is younger. The results showed evidence that the age of the authors increased discrimination, with men being more likely to use non-inclusive terms (up to an index of 23.12), showing that there is a greater awareness of inclusiveness in women than in men in all age ranges (with an average of 14.99), and also that this awareness grows as the candidate is younger (falling down to 13.07). In terms of field of knowledge, the humanities are the most biased (20.97), discarding the subgroup of Linguistics, which has the least bias at all levels (9.90), and the field of science and engineering, which also have the least influence (13.46). Those results support the assumption that the bias in academic texts (doctoral theses) is due to unconscious issues: otherwise, it would not depend on the field, age, gender, and would occur in any field in the same proportion. The innovation provided by this research lies mainly in the ability to detect, within a textual document in Spanish, whether the use of language can be considered non-inclusive, based on a CNN that has been trained in the context of the doctoral thesis. A significant number of documents have been used, using all accessible doctoral theses from Spanish universities of the last 40 years; this dataset is only manageable by data mining systems, so that the training allows identifying the terms within the context effectively and compiling them in a novel dictionary of non-inclusive terms.Pedro Orgeira-CrespoCarla Míguez-ÁlvarezMiguel Cuevas-AlonsoElena Rivo-LópezPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0257903 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Pedro Orgeira-Crespo Carla Míguez-Álvarez Miguel Cuevas-Alonso Elena Rivo-López An analysis of unconscious gender bias in academic texts by means of a decision algorithm.
description	Inclusive language focuses on using the vocabulary to avoid exclusion or discrimination, specially referred to gender. The task of finding gender bias in written documents must be performed manually, and it is a time-consuming process. Consequently, studying the usage of non-inclusive language on a document, and the impact of different document properties (such as author gender, date of presentation, etc.) on how many non-inclusive instances are found, is quite difficult or even impossible for big datasets. This research analyzes the gender bias in academic texts by analyzing a study corpus of more than 12,000 million words obtained from more than one hundred thousand doctoral theses from Spanish universities. For this purpose, an automated algorithm was developed to evaluate the different characteristics of the document and look for interactions between age, year of publication, gender or the field of knowledge in which the doctoral thesis is framed. The algorithm identified information patterns using a CNN (convolutional neural network) by the creation of a vector representation of the sentences. The results showed evidence that there was a greater bias as the age of the authors increased, who were more likely to use non-inclusive terms; it was concluded that there is a greater awareness of inclusiveness in women than in men, and also that this awareness grows as the candidate is younger. The results showed evidence that the age of the authors increased discrimination, with men being more likely to use non-inclusive terms (up to an index of 23.12), showing that there is a greater awareness of inclusiveness in women than in men in all age ranges (with an average of 14.99), and also that this awareness grows as the candidate is younger (falling down to 13.07). In terms of field of knowledge, the humanities are the most biased (20.97), discarding the subgroup of Linguistics, which has the least bias at all levels (9.90), and the field of science and engineering, which also have the least influence (13.46). Those results support the assumption that the bias in academic texts (doctoral theses) is due to unconscious issues: otherwise, it would not depend on the field, age, gender, and would occur in any field in the same proportion. The innovation provided by this research lies mainly in the ability to detect, within a textual document in Spanish, whether the use of language can be considered non-inclusive, based on a CNN that has been trained in the context of the doctoral thesis. A significant number of documents have been used, using all accessible doctoral theses from Spanish universities of the last 40 years; this dataset is only manageable by data mining systems, so that the training allows identifying the terms within the context effectively and compiling them in a novel dictionary of non-inclusive terms.
format	article
author	Pedro Orgeira-Crespo Carla Míguez-Álvarez Miguel Cuevas-Alonso Elena Rivo-López
author_facet	Pedro Orgeira-Crespo Carla Míguez-Álvarez Miguel Cuevas-Alonso Elena Rivo-López
author_sort	Pedro Orgeira-Crespo
title	An analysis of unconscious gender bias in academic texts by means of a decision algorithm.
title_short	An analysis of unconscious gender bias in academic texts by means of a decision algorithm.
title_full	An analysis of unconscious gender bias in academic texts by means of a decision algorithm.
title_fullStr	An analysis of unconscious gender bias in academic texts by means of a decision algorithm.
title_full_unstemmed	An analysis of unconscious gender bias in academic texts by means of a decision algorithm.
title_sort	analysis of unconscious gender bias in academic texts by means of a decision algorithm.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/ac933f33d8a74ac2bf1204b25abfb9ca
work_keys_str_mv	AT pedroorgeiracrespo ananalysisofunconsciousgenderbiasinacademictextsbymeansofadecisionalgorithm AT carlamiguezalvarez ananalysisofunconsciousgenderbiasinacademictextsbymeansofadecisionalgorithm AT miguelcuevasalonso ananalysisofunconsciousgenderbiasinacademictextsbymeansofadecisionalgorithm AT elenarivolopez ananalysisofunconsciousgenderbiasinacademictextsbymeansofadecisionalgorithm AT pedroorgeiracrespo analysisofunconsciousgenderbiasinacademictextsbymeansofadecisionalgorithm AT carlamiguezalvarez analysisofunconsciousgenderbiasinacademictextsbymeansofadecisionalgorithm AT miguelcuevasalonso analysisofunconsciousgenderbiasinacademictextsbymeansofadecisionalgorithm AT elenarivolopez analysisofunconsciousgenderbiasinacademictextsbymeansofadecisionalgorithm
_version_	1718374728597504000

An analysis of unconscious gender bias in academic texts by means of a decision algorithm.

Ejemplares similares