# **1. Introduction**

Artificial intelligence (AI) enables computers and machines to mimic human thinking, problemsolving, and social skills (Haenlein and Kaplan, 2019). We encounter AI in our daily lives through digital assistants, GPS navigation, self-driving cars, and generative tools like OpenAI’s ChatGPT. Many see AI as a catalyst for economic growth, impacting various sectors, from consumer goods to health care (Shrestha and Das, 2022). However, this promise also raises significant concerns, particularly regarding how AI can perpetuate societal inequalities and injustices linked to factors such as gender, race, ethnicity, or age. This paper specifically examines how bias in AI, rooted in social inequality, can hinder our understanding of citizen participation, a vital element of democracy.

While previous research has explored citizen participation, few studies have employed AI methods. The available evidence indicates that participation is a complex and nonlinear behavior that traditional linear statistical methods often oversimplify. For example, Pecorari and Cuesta (2024) highlight potential misinterpretations of participation patterns in Latin America. One of those is that women are generally less likely to engage in various forms of civic participation— such as signing petitions, or voting (which is not corroborated by data from Latinobarometro 2024).

Although the case for applying AI methods to study participation is compelling, little attention has been paid to how biases related to gender, gender identity, and sexual orientation impact research findings and the policy making that follows. We propose a two-part working hypothesis: first, that gender bias significantly affects our ability to predict citizen participation; and second, that all forms of gender bias—societal, data, and algorithmic—are crucial in shaping these predictions.

This paper contributes to the literature by identifying, assessing, and comparing different gender biases in the context of AI's analysis of social issues, rather than considering them separately as previous studies discussing AI biases have done. It is important to note that AI is closely linked to machine learning and deep learning: machine learning focuses on creating algorithms that improve predictions by learning from structured, labeled data, while deep learning uses unsupervised techniques to extract insights from large, unlabeled datasets (Gupta, Parra, and Dennehy, 2022). Although AI encompasses more than these two approaches, much of the literature emphasizes machine learning and its implications for fairness in research. Our analysis shares this emphasis.

The structure of this paper is as follows: Section 2 reviews the definitions and classifications of gender bias in AI, discussing their causes and potential mitigation strategies. Section 3 explains why decision trees are chosen as the preferred AI method for analyzing citizen participation and details how to operationalize societal, data, and algorithmic gender-related biases for comparison. Section 4 presents key predictions regarding citizen participation, focusing on how machismo societal norms influence engagement. Finally, Section 5 discusses the findings and concludes the paper.

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