Abstract

This study presents a Systematic Literature Review (SLR) of Gender Bias in Artificial Intelligence (AI). The research was conducted using two techniques: a domain-based approach to SLR process providing a bibliometric sample description and in-depth examination of the thematic categories arising from inductive categorization, extracted from reading and interpretation of the final 35 sample articles analyzed. In answering three key research questions on the types, causes, and overcoming (mitigating) strategies of gender bias in artificial intelligence, three thematic treemaps were constructed, enabling systematic overview as an essential contribution to the literature. The main types of gender bias found in AI are categorized as societal, technical, and individual. Societal and socio-technical aspects stand out as the leading causes of bias, while debiasing, dataset design and gender sensitivity were the most frequent among the main strategies for overcoming bias. The study also proposes theoretical, practical and managerial capacity building and policy implications that aim to influence broad socio-technical challenges and refer to changes necessary, aiming to create bias-free artificial intelligence.