Viés e Discriminação no Uso de Aprendizado de Máquina em Governo Digital: Uma Revisão da Literatura
Resumo
A adoção crescente de Inteligência Artificial (IA) no setor público tem ampliado a eficiência e a precisão de políticas e serviços públicos. Entretanto, essas tecnologias podem reproduzir desigualdades históricas presentes nos dados, levantando preocupações relacionadas a viés, justiça e discriminação em áreas críticas como justiça criminal, saúde, benefícios sociais e compras públicas. Apesar da ampla literatura sobre o uso de IA no setor público e das diversas propostas de mitigação de vieses, ainda há escassez de estudos sistemáticos que analisem especificamente como esses vieses são tratados no contexto do governo digital. Essa lacuna limita a compreensão de como governos, órgãos de controle e a sociedade podem garantir sistemas mais justos, transparentes e alinhados à prestação de serviços públicos. Este trabalho apresenta uma Revisão Sistemática da Literatura (RSL) sobre como a pesquisa acadêmica aborda o viés e a discriminação em sistemas de IA aplicados ao setor público. A revisão segue o protocolo de Kitchenham e o modelo PRISMA, considerando estudos das bases Scopus e IEEE Xplore, com 372 publicações iniciais e 31 trabalhos na análise final. Os resultados identificam estratégias como balanceamento de dados, uso de dados sintéticos e modelos interpretáveis. Contudo, a maioria das abordagens permanece em nível experimental, com limitações de replicabilidade e pouca consideração de variáveis sensíveis e de contextos reais de aplicação. Os achados evidenciam uma lacuna entre soluções técnicas e sua aplicação prática no contexto governamental. O estudo destaca a necessidade de abordagens auditáveis, reprodutíveis e socialmente orientadas, posicionando a justiça algorítmica como um requisito fundamental para um governo digital confiável e centrado no cidadão.
Referências
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Grupo de Peritos de Alto Nível sobre Inteligência Artificial (2019). Orientações Éticas para uma ia de confiança. Relatório técnico, Comissão Europeia. Disponível em: [link].
Guevara-Gómez, A., de Zárate-Alcarazo, L. O., and Criado, J. I. (2021). Feminist perspectives to artificial intelligence: Comparing the policy frames of the european union and spain. Information Polity, 26(2):173–192.
Hoekstra, M., Van Veenstra, A. F., and Chideock, C. (2021). A typology for applications of public sector ai. In EGOV-CeDEM-ePart-*, pages 121–128.
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Landers, R. N. and Behrend, T. S. (2023). Auditing the ai auditors: A framework for evaluating fairness and bias in high stakes ai predictive models. American Psychologist, 78(1):36.
Levy, K., Chasalow, K. E., and Riley, S. (2021). Algorithms and decision-making in the public sector. Annual Review of Law and Social Science, 17(1):309–334.
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Malhotra, P. and Misra, A. (2022). Accountability and responsibility of artificial intelligence decision-making models in indian policy landscape. In AISafety@ IJCAI.
McCradden, M. D., Joshi, S., Anderson, J. A., Mazwi, M., Goldenberg, A., and Zlotnik Shaul, R. (2020). Patient safety and quality improvement: Ethical principles for a regulatory approach to bias in healthcare machine learning. Journal of the American Medical Informatics Association, 27(12):2024–2027.
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Mitrou, L., Janssen, M., and Loukis, E. (2021). Human control and discretion in ai-driven decision-making in government. In Proceedings of the 14th International Conference on Theory and Practice of Electronic Governance, pages 10–16.
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., and Group*, P. (2009). Preferred reporting items for systematic reviews and meta-analyses: the prisma statement. Annals of internal medicine, 151(4):264–269.
Nascimento, F. R., Cavalcanti, G. D., and Da Costa-Abreu, M. (2022). Unintended bias evaluation: An analysis of hate speech detection and gender bias mitigation on social media using ensemble learning. Expert Systems with Applications, 201:117032.
Newman-Griffis, D., Rauchberg, J. S., Alharbi, R., Hickman, L., and Hochheiser, H. (2022). Definition drives +design: Disability models and mechanisms of bias in ai technologies. arXiv preprint arXiv:2206.08287.
Nwafor, I. E. (2024). Gender mainstreaming into african artificial intelligence policies: Egypt, rwanda and mauritius as case studies. Law, Technology and Humans, 6(2):53–68.
O’Donnell, R. M. (2019). Challenging racist predictive policing algorithms under the equal protection clause. NYUL Rev., 94:544.
Oniani, D., Hilsman, J., Peng, Y., Poropatich, R. K., Pamplin, J. C., Legault, G. L., and Wang, Y. (2023). Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare. NPJ Digital Medicine, 6(1):225.
Patrikar, A. M., Mahenthiran, A., and Said, A. (2023). Leveraging synthetic data for ai bias mitigation. In Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications, volume 12529, pages 185–190. SPIE.
PENG, L., ZHANG, Q., and LI, T. (2024). Risk of ai algorithmic discrimination embedded in government data governance and its prevention and control. Journal of library and information science in agriculture, 36(5):23–31.
Perez-Downes, J. C., Tseng, A. S., McConn, K. A., Elattar, S. M., Sokumbi, O., Sebro, R. A., Allyse, M. A., Dangott, B. J., Carter, R. E., and Adedinsewo, D. (2024). Mitigating bias in clinical machine learning models. Current Treatment Options in Cardiovascular Medicine, 26(3):29–45.
Pérez-Suay, A., Laparra, V., Mateo-García, G., Muñoz-Marí, J., Gómez-Chova, L., and Camps-Valls, G. (2017). Fair kernel learning. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 339–355. Springer.
Rodriguez, J. A., Alsentzer, E., and Bates, D. W. (2024). Leveraging large language models to foster equity in healthcare. Journal of the American Medical Informatics Association, 31(9):2147–2150.
Torres-Berru, Y., Lopez-Batista, V. F., and Zhingre, L. C. (2023). A data mining approach to detecting bias and favoritism in public procurement. Intelligent Automation & Soft Computing, 36(3).
Turner Lee, N. (2018). Detecting racial bias in algorithms and machine learning. Journal of Information, Communication and Ethics in Society, 16(3):252–260.
Utomo, S., John, A., Rouniyar, A., Hsu, H.-C., and Hsiung, P.-A. (2022). Federated trustworthy ai architecture for smart cities. In 2022 IEEE International Smart Cities Conference (ISC2), pages 1–7. IEEE.
Valle-Cruz, D., Alejandro Ruvalcaba-Gomez, E., Sandoval-Almazan, R., and Ignacio Criado, J. (2019). A review of artificial intelligence in government and its potential from a public policy perspective. In Proceedings of the 20th annual international conference on digital government research, pages 91–99.
Vetrò, A., Santangelo, A., Beretta, E., and De Martin, J. C. (2019). Ai: from rational agents to socially responsible agents. Digital policy, regulation and governance, 21(3):291–304.
Wei, M. and Zhou, Z. (2022). Ai ethics issues in real world: Evidence from ai incident database. arXiv preprint arXiv:2206.07635.
Zajko, M. (2022). Artificial intelligence, algorithms, and social inequality: Sociological contributions to contemporary debates. Sociology Compass, 16(3):e12962.
Zhao, L. F. (2020). Data-driven approach for predicting and explaining the risk of long-term unemployment. In E3S Web of Conferences, volume 214, page 01023. EDP Sciences.
Balayn, A., Lofi, C., and Houben, G.-J. (2021). Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and unfairness within data management and analytics systems. The VLDB Journal, 30(5):739–768.
Bannister, F. and Connolly, R. (2020). Administration by algorithm: A risk management framework. Information Polity, 25(4):471–490.
Cheng, L., Gallegos, I. O., Ouyang, D., Goldin, J., and Ho, D. (2023). How redundant are redundant encodings? blindness in the wild and racial disparity when race is unobserved. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, pages 667–686.
Fountain, J. E. (2022). The moon, the ghetto and artificial intelligence: Reducing systemic racism in computational algorithms. Government Information Quarterly, 39(2):101645.
Gao, J., Chu, D., and Ye, T. (2021). Empirical analysis of beneficial equality of the basic medical insurance for migrants in china. Discrete Dynamics in Nature and Society, 2021(1):7708605.
Gentzel, M. (2021). Biased face recognition technology used by government: A problem for liberal democracy. Philosophy & Technology, 34(4):1639–1663.
Grupo de Peritos de Alto Nível sobre Inteligência Artificial (2019). Orientações Éticas para uma ia de confiança. Relatório técnico, Comissão Europeia. Disponível em: [link].
Guevara-Gómez, A., de Zárate-Alcarazo, L. O., and Criado, J. I. (2021). Feminist perspectives to artificial intelligence: Comparing the policy frames of the european union and spain. Information Polity, 26(2):173–192.
Hoekstra, M., Van Veenstra, A. F., and Chideock, C. (2021). A typology for applications of public sector ai. In EGOV-CeDEM-ePart-*, pages 121–128.
Johnson, K. N. and Reyes, C. L. (2021). Exploring the implications of artificial intelligence. J. Int’l & Comp. L., 8:315.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004):1–26.
Landers, R. N. and Behrend, T. S. (2023). Auditing the ai auditors: A framework for evaluating fairness and bias in high stakes ai predictive models. American Psychologist, 78(1):36.
Levy, K., Chasalow, K. E., and Riley, S. (2021). Algorithms and decision-making in the public sector. Annual Review of Law and Social Science, 17(1):309–334.
Liu, H.-W., Lin, C.-F., and Chen, Y.-J. (2019). Beyond state v loomis: artificial intelligence, government algorithmization and accountability. International journal of law and information technology, 27(2):122–141.
Lucas, M. M., Wang, X., Chang, C.-H., Yang, C. C., Braughton, J. E., and Ngo, Q. M. (2024). An explainablefair framework for prediction of substance use disorder treatment completion. In 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI), pages 157–166. IEEE.
Malhotra, P. and Misra, A. (2022). Accountability and responsibility of artificial intelligence decision-making models in indian policy landscape. In AISafety@ IJCAI.
McCradden, M. D., Joshi, S., Anderson, J. A., Mazwi, M., Goldenberg, A., and Zlotnik Shaul, R. (2020). Patient safety and quality improvement: Ethical principles for a regulatory approach to bias in healthcare machine learning. Journal of the American Medical Informatics Association, 27(12):2024–2027.
Mergen, A., Çetin-Kılıç, N., and Özbilgin, M. F. (2025). Artificial intelligence and bias towards marginalised groups: Theoretical roots and challenges. In AI and Diversity in a Datafied World of Work: Will the Future of Work be Inclusive?, volume 12, pages 17–38. Emerald Publishing Limited.
Mitrou, L., Janssen, M., and Loukis, E. (2021). Human control and discretion in ai-driven decision-making in government. In Proceedings of the 14th International Conference on Theory and Practice of Electronic Governance, pages 10–16.
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., and Group*, P. (2009). Preferred reporting items for systematic reviews and meta-analyses: the prisma statement. Annals of internal medicine, 151(4):264–269.
Nascimento, F. R., Cavalcanti, G. D., and Da Costa-Abreu, M. (2022). Unintended bias evaluation: An analysis of hate speech detection and gender bias mitigation on social media using ensemble learning. Expert Systems with Applications, 201:117032.
Newman-Griffis, D., Rauchberg, J. S., Alharbi, R., Hickman, L., and Hochheiser, H. (2022). Definition drives +design: Disability models and mechanisms of bias in ai technologies. arXiv preprint arXiv:2206.08287.
Nwafor, I. E. (2024). Gender mainstreaming into african artificial intelligence policies: Egypt, rwanda and mauritius as case studies. Law, Technology and Humans, 6(2):53–68.
O’Donnell, R. M. (2019). Challenging racist predictive policing algorithms under the equal protection clause. NYUL Rev., 94:544.
Oniani, D., Hilsman, J., Peng, Y., Poropatich, R. K., Pamplin, J. C., Legault, G. L., and Wang, Y. (2023). Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare. NPJ Digital Medicine, 6(1):225.
Patrikar, A. M., Mahenthiran, A., and Said, A. (2023). Leveraging synthetic data for ai bias mitigation. In Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications, volume 12529, pages 185–190. SPIE.
PENG, L., ZHANG, Q., and LI, T. (2024). Risk of ai algorithmic discrimination embedded in government data governance and its prevention and control. Journal of library and information science in agriculture, 36(5):23–31.
Perez-Downes, J. C., Tseng, A. S., McConn, K. A., Elattar, S. M., Sokumbi, O., Sebro, R. A., Allyse, M. A., Dangott, B. J., Carter, R. E., and Adedinsewo, D. (2024). Mitigating bias in clinical machine learning models. Current Treatment Options in Cardiovascular Medicine, 26(3):29–45.
Pérez-Suay, A., Laparra, V., Mateo-García, G., Muñoz-Marí, J., Gómez-Chova, L., and Camps-Valls, G. (2017). Fair kernel learning. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 339–355. Springer.
Rodriguez, J. A., Alsentzer, E., and Bates, D. W. (2024). Leveraging large language models to foster equity in healthcare. Journal of the American Medical Informatics Association, 31(9):2147–2150.
Torres-Berru, Y., Lopez-Batista, V. F., and Zhingre, L. C. (2023). A data mining approach to detecting bias and favoritism in public procurement. Intelligent Automation & Soft Computing, 36(3).
Turner Lee, N. (2018). Detecting racial bias in algorithms and machine learning. Journal of Information, Communication and Ethics in Society, 16(3):252–260.
Utomo, S., John, A., Rouniyar, A., Hsu, H.-C., and Hsiung, P.-A. (2022). Federated trustworthy ai architecture for smart cities. In 2022 IEEE International Smart Cities Conference (ISC2), pages 1–7. IEEE.
Valle-Cruz, D., Alejandro Ruvalcaba-Gomez, E., Sandoval-Almazan, R., and Ignacio Criado, J. (2019). A review of artificial intelligence in government and its potential from a public policy perspective. In Proceedings of the 20th annual international conference on digital government research, pages 91–99.
Vetrò, A., Santangelo, A., Beretta, E., and De Martin, J. C. (2019). Ai: from rational agents to socially responsible agents. Digital policy, regulation and governance, 21(3):291–304.
Wei, M. and Zhou, Z. (2022). Ai ethics issues in real world: Evidence from ai incident database. arXiv preprint arXiv:2206.07635.
Zajko, M. (2022). Artificial intelligence, algorithms, and social inequality: Sociological contributions to contemporary debates. Sociology Compass, 16(3):e12962.
Zhao, L. F. (2020). Data-driven approach for predicting and explaining the risk of long-term unemployment. In E3S Web of Conferences, volume 214, page 01023. EDP Sciences.
Publicado
19/07/2026
Como Citar
COUTINHO, Gabriela B.; SAMPAIO, Igor G. B.; BARCELLOS, Raissa; BERNARDINI, Flavia.
Viés e Discriminação no Uso de Aprendizado de Máquina em Governo Digital: Uma Revisão da Literatura. In: LATIN AMERICAN SYMPOSIUM ON DIGITAL GOVERNMENT (LASDIGOV), 14. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 313-324.
ISSN 2763-8723.
DOI: https://doi.org/10.5753/lasdigov.2026.23204.
