Why are women not promoted as quickly or as high as men? Why are women paid less than their male counterparts for similar roles and responsibilities? Why is bridging the gender gap so difficult? For instance, in European Union countries in 2020 women’s gross hourly earnings were on average 13% below those of men, and 2018 US Census Bureau data says women of all races earned, on average, just 82 cents for every dollar earned by men of all races. Gender disparities, though, are much more severe and pervasive, as stated in the latest UN Women report (bit.ly/3Kc7knX): “Gender inequalities and discrimination filter through every issue, whether a new pandemic or longstanding conflicts, deep-seated disparities in income or a lack of political voice. Women and girls confront additional risks and obstacles simply because they are women and girls.” Gender equality, in all aspects of women’s and men’s lives, is a democratic value and a prominent sustainable development goal in the United Nations (UN) Agenda 2030. Gendered statistical reasoning and gender statistics play a crucial role in informing policies and the efforts towards achieving equality, as well as in allocation of resources, and the potential to effectively contribute towards the protection and advancement of the rights of all women and girls. In this article we illustrate the importance of improving the measurement, monitoring, and prediction of gender gaps by exploring the potential of Bayesian networks.

Mecatti, F., Vicard, P., Musella, F., Giammei, L. (2022). Bayesian networks versus gender bias. SIGNIFICANCE, 19(5), 16-20 [10.1111/1740-9713.01684].

Bayesian networks versus gender bias

Vicard Paola;
2022

Abstract

Why are women not promoted as quickly or as high as men? Why are women paid less than their male counterparts for similar roles and responsibilities? Why is bridging the gender gap so difficult? For instance, in European Union countries in 2020 women’s gross hourly earnings were on average 13% below those of men, and 2018 US Census Bureau data says women of all races earned, on average, just 82 cents for every dollar earned by men of all races. Gender disparities, though, are much more severe and pervasive, as stated in the latest UN Women report (bit.ly/3Kc7knX): “Gender inequalities and discrimination filter through every issue, whether a new pandemic or longstanding conflicts, deep-seated disparities in income or a lack of political voice. Women and girls confront additional risks and obstacles simply because they are women and girls.” Gender equality, in all aspects of women’s and men’s lives, is a democratic value and a prominent sustainable development goal in the United Nations (UN) Agenda 2030. Gendered statistical reasoning and gender statistics play a crucial role in informing policies and the efforts towards achieving equality, as well as in allocation of resources, and the potential to effectively contribute towards the protection and advancement of the rights of all women and girls. In this article we illustrate the importance of improving the measurement, monitoring, and prediction of gender gaps by exploring the potential of Bayesian networks.
Mecatti, F., Vicard, P., Musella, F., Giammei, L. (2022). Bayesian networks versus gender bias. SIGNIFICANCE, 19(5), 16-20 [10.1111/1740-9713.01684].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/419068
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