Algorithmic Fairness in Digital Governance: Mitigating Bias for Marginalized Groups in Latin America

Abstract:

As Latin American countries accelerate digital governance—integrating algorithms into public service allocation, judicial processes, and social protection—marginalized groups (including Indigenous populations, low-income households, Afro-Latin communities, and persons with disabilities) face heightened risks of algorithmic bias and exclusion. This study examines how algorithmic systems in four key governance areas (social welfare, criminal justice, healthcare access, and education) perpetuate or mitigate inequalities across six Latin American countries (Chile, Mexico, Brazil, Argentina, Colombia, and Peru). Through a mixed-methods approach involving algorithmic impact audits of 18 digital governance tools, 76 stakeholder interviews, and policy analysis of 35 national frameworks, the research identifies three primary sources of algorithmic bias: unrepresentative training data, structural inequality embedded in input variables, and limited transparency in algorithmic decision-making. Findings reveal that bias-mitigating governance strategies—including participatory algorithm design, mandatory fairness audits, and community-led oversight mechanisms—reduce discriminatory outcomes by 43% compared to unregulated algorithmic systems. The study contributes to global debates on algorithmic justice by proposing a “Latin American Algorithmic Fairness Framework” that integrates contextual realities (e.g., historical inequality, cultural diversity, and variable institutional capacity) into bias mitigation, offering actionable recommendations for policymakers, tech developers, and civil society organizations seeking to ensure digital governance serves all Latin Americans equitably.