of reducing machismo and assess how these changes impact the success path to citizen participation. This approach also allows us to evaluate the robustness of the results regarding the influence of reduced machismo on participation.

These methods are informed by the review of technical strategies to mitigate gender biases in AI presented earlier. The gender blinding, deproxing, and shifting/scaling strategies align with data and algorithmic bias mitigation techniques in the literature, such as adversarial debiasing and counterfactual data augmentation, aimed at reducing gender bias in AI models. The WOMENonly and MEN-only models further explore the literature's emphasis on examining genderspecific bias and ensuring fairness across demographic groups, consistent with recommendations for balanced datasets and inclusive AI. The focus on societal gender bias as an overarching factor aligns with calls for considering broader social contexts in AI assessments, as highlighted by Baeza-Yates (2018) and Smith and Rustagi (2021), who stress the need for algorithms to account for societal biases.

# **_3.3 Data and variables_**

This study uses data from the 2023 Latinobarómetro Survey (Latinobarómetro Corporation 2023), an annual survey designed to assess individual perceptions of socioeconomic and political issues. Covering 17 countries in Latin America and the Caribbean, it is the largest regional database on citizen attitudes toward democracy. The survey employs a stratified random sampling method, weighted to accurately represent each country's population. The latest data available during the preparation of this study, released in December 2023, includes 19,205 observations, representing over 600 million people in the region. However, it does not include data from Nicaragua, Haiti, Trinidad and Tobago, or other small Caribbean nations. Data collection was conducted face-to-face from February 20 to April 18, 2023.

The dependent variable in this study is citizen participation, specifically in the context of working to address community problems. The primary independent variable is a composite gender bias score, derived as the first component of a principal component analysis (PCA) based on four indicators that capture the prevalence of gender-biased social norms in the country of analysis: (i) women should focus on domestic roles while men should work; (ii) men are better political leaders than women; (iii) if a woman earns more than a man, she is likely to face difficulties; and (iv) in times of scarce jobs, men should have a greater right to employment than women.

Each of these four gender bias indicators is coded as 1 if the respondent agreed or strongly agreed, and 0 otherwise. The composite gender bias score is then normalized on a scale from 0 (low machismo) to 1 (high machismo). By using the first component of principal component analysis (PCA), we derive a single variable that captures the combined variability of the four gender bias indicators, simplifying the analysis of how gender bias influences individual decisions to engage in solving community problems.

The decision to use the first principal component from the PCA is based on two widely used, independent methods. First, the Kaiser-Guttman criterion, which recommends retaining components with an eigenvalue greater than one, supports using the first component (Kaiser, 1960; Guttman, 1954). Second, the elbow method, a visual approach, indicates that the first 11