Measuring the gender discrimination: A machine learning approach

Hugo Alatrista-Salas; Bruno Esposito; Miguel Nunez-Del-Prado; Maria Valdivieso

doi:10.1109/LA-CCI.2017.8285682

Measuring the gender discrimination: A machine learning approach

Hugo Alatrista-Salas
, Bruno Esposito
, Miguel Nunez-Del-Prado
, Maria Valdivieso

Universidad del Pacífico

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

3 Citas (Scopus)

Resumen

Gender discrimination is a widely analyzed problem, which seems to affect different countries and cultures over time. Nowadays, we are witnesses of the social inequality reflected by the salary difference between women and men for the same employment. Since the incorporation of women into the labor market in the 1980s, the wage gap between males and females has been a subject of study. One of the traditional arguments has been linked to the feminized occupations associated with sex stereotypes, as well as, low wage, birth, and discrimination in the labor categories. In the present work, we apply clustering algorithms to the PHOGUE dataset to analyze salary difference between males and females in Spain and England.

Idioma original	Inglés
Título de la publicación alojada	2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	1-6
Número de páginas	6
ISBN (versión digital)	9781538637340
DOI	https://doi.org/10.1109/LA-CCI.2017.8285682
Estado	Publicada - 2 jul. 2017
Publicado de forma externa	Sí
Evento	2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Arequipa, Perú Duración: 8 nov. 2017 → 10 nov. 2017

Serie de la publicación

Nombre	2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings
Volumen	2017-November

Conferencia

Conferencia	2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017
País/Territorio	Perú
Ciudad	Arequipa
Período	8/11/17 → 10/11/17

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 5: Igualdad de género

Acceder al documento

10.1109/LA-CCI.2017.8285682

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Alatrista-Salas, H., Esposito, B., Nunez-Del-Prado, M., & Valdivieso, M. (2017). Measuring the gender discrimination: A machine learning approach. En 2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings (pp. 1-6). (2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings; Vol. 2017-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/LA-CCI.2017.8285682

Alatrista-Salas, Hugo ; Esposito, Bruno ; Nunez-Del-Prado, Miguel et al. / Measuring the gender discrimination : A machine learning approach. 2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1-6 (2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings).

@inproceedings{a83e9ac386b34c028769b9bd797d9bdf,

title = "Measuring the gender discrimination: A machine learning approach",

abstract = "Gender discrimination is a widely analyzed problem, which seems to affect different countries and cultures over time. Nowadays, we are witnesses of the social inequality reflected by the salary difference between women and men for the same employment. Since the incorporation of women into the labor market in the 1980s, the wage gap between males and females has been a subject of study. One of the traditional arguments has been linked to the feminized occupations associated with sex stereotypes, as well as, low wage, birth, and discrimination in the labor categories. In the present work, we apply clustering algorithms to the PHOGUE dataset to analyze salary difference between males and females in Spain and England.",

keywords = "Data Mining, Expectation Maximization, Optics, clustering, gender discrimination",

author = "Hugo Alatrista-Salas and Bruno Esposito and Miguel Nunez-Del-Prado and Maria Valdivieso",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 ; Conference date: 08-11-2017 Through 10-11-2017",

year = "2017",

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Alatrista-Salas, H, Esposito, B, Nunez-Del-Prado, M & Valdivieso, M 2017, Measuring the gender discrimination: A machine learning approach. En 2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings. 2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings, vol. 2017-November, Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017, Arequipa, Perú, 8/11/17. https://doi.org/10.1109/LA-CCI.2017.8285682

Measuring the gender discrimination: A machine learning approach. / Alatrista-Salas, Hugo; Esposito, Bruno; Nunez-Del-Prado, Miguel et al.
2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1-6 (2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings; Vol. 2017-November).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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T1 - Measuring the gender discrimination

T2 - 2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017

AU - Alatrista-Salas, Hugo

AU - Esposito, Bruno

AU - Nunez-Del-Prado, Miguel

AU - Valdivieso, Maria

PY - 2017/7/2

Y1 - 2017/7/2

N2 - Gender discrimination is a widely analyzed problem, which seems to affect different countries and cultures over time. Nowadays, we are witnesses of the social inequality reflected by the salary difference between women and men for the same employment. Since the incorporation of women into the labor market in the 1980s, the wage gap between males and females has been a subject of study. One of the traditional arguments has been linked to the feminized occupations associated with sex stereotypes, as well as, low wage, birth, and discrimination in the labor categories. In the present work, we apply clustering algorithms to the PHOGUE dataset to analyze salary difference between males and females in Spain and England.

AB - Gender discrimination is a widely analyzed problem, which seems to affect different countries and cultures over time. Nowadays, we are witnesses of the social inequality reflected by the salary difference between women and men for the same employment. Since the incorporation of women into the labor market in the 1980s, the wage gap between males and females has been a subject of study. One of the traditional arguments has been linked to the feminized occupations associated with sex stereotypes, as well as, low wage, birth, and discrimination in the labor categories. In the present work, we apply clustering algorithms to the PHOGUE dataset to analyze salary difference between males and females in Spain and England.

KW - Data Mining

KW - Expectation Maximization

KW - Optics

KW - clustering

KW - gender discrimination

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M3 - Conference contribution

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T3 - 2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings

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Alatrista-Salas H, Esposito B, Nunez-Del-Prado M, Valdivieso M. Measuring the gender discrimination: A machine learning approach. En 2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1-6. (2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings). doi: 10.1109/LA-CCI.2017.8285682

Measuring the gender discrimination: A machine learning approach

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