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

Research output: Contribution to conference › Paper

2 Scopus citations

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.

Original language	English
Pages	1-6
Number of pages	6
DOIs	https://doi.org/10.1109/LA-CCI.2017.8285682
State	Published - 7 Feb 2018
Event	2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings - Duration: 7 Feb 2018 → …

Conference

Conference	2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings
Period	7/02/18 → …

Keywords

Data Mining
Expectation Maximization
Optics
clustering
gender discrimination

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/LA-CCI.2017.8285682

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AU - Esposito, Bruno

AU - Nunez-Del-Prado, Miguel

AU - Valdivieso, Maria

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KW - Expectation Maximization

KW - Optics

KW - clustering

KW - gender discrimination

KW - Data Mining

KW - Expectation Maximization

KW - Optics

KW - clustering

KW - gender discrimination

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ER -

Measuring the gender discrimination: A machine learning approach

Abstract

Conference

Keywords

UN SDGs

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