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This paper investigates the role of gender bias in artificial intelligence–driven analyses of citizen participation, using data from the 2023 Latinobarómetro Survey. The paper proposes that gender bias—whether societal, data driven, or algorithmic—significantly affects civic engagement. Using machine learning, particularly decision trees, the analysis explores how self-reported societal bias (machismo norms) interacts with personal characteristics and circumstances to shape civic participation. The findings show that individuals with reportedly low levels of gender bias, who express political interest, have high levels of education, and align with left-wing views, are more likely to participate. The paper also explores different strategies to mitigate gender bias in both the data and the algorithms, demonstrating that gender bias remains a persistent factor even after applying corrective measures. Notably, lower machismo thresholds are required for participation in more egalitarian societies, with men needing to exhibit especially low machismo levels. Ultimately, the findings emphasize the importance of integrated strategies to tackle gender bias and increase participation, offering a framework for future studies to expand on nonlinear and complex social dynamics.