Artificial intelligence, algorithmic bias and indirect discrimination in health. An international normative analysis (2016–2025)

Authors

  • Sofia Gutiérrez Pérez Investigadora independiente
  • Raúl Bermúdez Camarena Investigador independiente
  • Héctor Antonio Emiliano Magallanes Ramírez Investigador independiente

DOI:

https://doi.org/10.20318/universitas.2026.10528

Keywords:

algorithmic prioritization, indirect discrimination, algorithmic bias, artificial intelligence governance, health regulation

Abstract

This article analyzes how artificial intelligence systems used in clinical prioritization reproduce structural inequalities, generating patterns of indirect discrimination. Using a qualitative documentary-analytical design, binding legal frameworks enacted between 2016 and 2025 in the European Union, the United States, China, the United Kingdom, Brazil and Spain are examined. Results show accelerated regulatory growth since 2021, emphasizing data protection and transparency, yet persistent gaps remain in liability and bias auditing. The study concludes that health care automation requires mandatory algorithmic impact assessments and enforceable accountability mechanisms.

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Author Biographies

  • Sofia Gutiérrez Pérez, Investigadora independiente

    Doctora en Desarrollo Humano. Comisión Estatal de Derechos Humanos de Jalisco

  • Raúl Bermúdez Camarena, Investigador independiente

    Maestro en Derecho. Universidad de Guadalajara (México)

  • Héctor Antonio Emiliano Magallanes Ramírez, Investigador independiente

    Doctor en Ciencias Políticas por la Universidad de Guadalajara (México)

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Published

2026-06-29
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No. 49 (2026)

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How to Cite

Gutiérrez Pérez, Sofia, Raúl Bermúdez Camarena, and Héctor Antonio Emiliano Magallanes Ramírez. 2026. “Artificial Intelligence, Algorithmic Bias and Indirect Discrimination in Health. An International Normative Analysis (2016–2025)”. UNIVERSITAS. Revista De Filosofía, Derecho Y Política, no. 49 (June): 87-110. https://doi.org/10.20318/universitas.2026.10528.