PRECONCEITO CODIFICADO: O VIÉS ALGORÍTMICO E SEUS IMPACTOS NA DESIGUALDADE DE GÊNERO

This study examines how artificial intelligence (AI) algorithms reproduce gender-based discriminatory patterns and their social impacts. The growing adoption of AI in sectors such as labor markets, healthcare, and public security raises concerns about the perpetuation of structural inequalities, particularly against women. Based on a literature review and case studies, the research identifies sectors most susceptible to algorithmic discrimination and assesses international mitigation practices. The findings indicate that algorithmic bias stems from unbalanced training data, lack of diversity in development teams, and the absence of adequate regulation.

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Publication Details

Journal
Revista Gênero
Published
2025-09-25
DOI
https://doi.org/10.22409/n6ncr397
Primary Topic
Income, Poverty, and Inequality
Type
article
Field-Weighted Citation Impact
0.00
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article

PRECONCEITO CODIFICADO: O VIÉS ALGORÍTMICO E SEUS IMPACTOS NA DESIGUALDADE DE GÊNERO

Sílvia Pimentel, Raíssa Amarins Marcandeli
Revista Gênero
Income, Poverty, and Inequality
article

PRECONCEITO CODIFICADO: O VIÉS ALGORÍTMICO E SEUS IMPACTOS NA DESIGUALDADE DE GÊNERO

Sílvia Pimentel, Raíssa Amarins Marcandeli
article en

Abstract

This study examines how artificial intelligence (AI) algorithms reproduce gender-based discriminatory patterns and their social impacts. The growing adoption of AI in sectors such as labor markets, healthcare, and public security raises concerns about the perpetuation of structural inequalities, particularly against women. Based on a literature review and case studies, the research identifies sectors most susceptible to algorithmic discrimination and assesses international mitigation practices. The findings indicate that algorithmic bias stems from unbalanced training data, lack of diversity in development teams, and the absence of adequate regulation.

Revista GêneroVol. 25(3)
Pontifícia Universidade Católica de São Paulo (BR)
Openalex Percentile: Top 65%
Income, Poverty, and Inequality
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