The Systemic Causes Behind Gender Bias in AI: A Systematic Literature Mapping

• Rafaela Toledo Dolabella UFV
• Thais Regina de Moura Braga Silva UFV
• Gláucia Braga e Silva UFV
• Estela Miranda Batista UFV

DOI: https://doi.org/10.5753/wit.2025.9141

Abstract

Artificial Intelligence (AI) has been transforming daily life, impacting critical decisions in various fields such as healthcare, security, and the labor market. However, its use raises ethical and social concerns, particularly regarding biases present in AI systems. This study specifically addresses gender bias, investigating the systemic causes (data, algorithmic bias, or user behavior) behind this issue through a Systematic Literature Mapping (SLM). The research analyzed 176 studies from the IEEE Xplore, ACM Digital Library, and Scopus databases, of which 12 were deemed relevant to the study’s objective. The selection process was guided by inclusion and exclusion criteria. The analysis identified that the primary cause of gender bias is the use of biased data, amplified by algorithmic bias in half of the studies. User behavior was minimally explored, being the main focus in only one article.

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