Governança algorítmica e ética na Inteligência: uma análise das iniciativas emergentes

Marjori Klinczak; Jose Simao de Paula Pinto

doi:10.58960/rbi.2026.21.291

Authors

Marjori Klinczak Centro Universitário de Tecnologia de Curitiba (Unifatec) https://orcid.org/0009-0009-2028-8359
Jose Simao de Paula Pinto Universidade Federal do Paraná https://orcid.org/0000-0002-5023-437X

DOI:

https://doi.org/10.58960/rbi.2026.21.291

Keywords:

algorithmic governance, artificial intelligence, biases, decision-making, algorithms

Abstract

Information systems and artificial intelligence are present in various aspects of daily life, such as communication, work, and education. However, this digital exposure enables the massive collection of data on personal habits and choices, often without explicit consent, which can be used for the creation of profiles, recommendation systems, and crime prevention. Nevertheless, the systems that process these data are not immune to biases, potentially affecting human rights, privacy, and freedom of expression. In this context, algorithmic governance seeks to promote greater transparency, ethics, and accountability in automated decision-making. The objective of this study is to analyze algorithmic and AI governance initiatives aimed at mitigating problems arising from biases and discrimination, through a bibliographic and exploratory methodology.

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Author Biographies

Marjori Klinczak, Centro Universitário de Tecnologia de Curitiba (Unifatec)

PhD candidate in Information Management at UFPR, she holds a Master's in Applied Computing from UTFPR. She specializes in digital law, development, ethical hacking, teaching, and finance. She holds degrees in Internet Systems from FAE, Financial Management from Fael, and Foreign Trade from Uninter. She works as a full-stack web/mobile developer, a forensic expert with a focus on IT, and a higher education professor.

Jose Simao de Paula Pinto, Universidade Federal do Paraná

Professor titular na Universidade Federal do Paraná (UFPR), onde foi diretor do Centro de Computação Eletrônica e coordenador da Pós-Graduação em Ciência, Gestão e Tecnologia da Informação. Atualmente leciona e pesquisa temas voltados à ciência e gestão da informação, às tecnologias da informação, gestão de projetos e das tecnologias como vetor da gestão estratégica. Coordena grupo de pesquisa em tecnologias e metodologias para a gestão da informação e do conhecimento. Coordena projeto de extensão em TI aplicada e programação de Arduino/MIT Inventor para crianças e jovens.

References

Aneesh, Anup. 2006. Virtual Migration: The Programming of Globalization. Durham: Duke University Press. DOI: https://doi.org/10.1515/9780822387534

Austin Jr., Roy; Fried, Ina. 2023. “Meta’s new yardstick for testing algorithmic bias”. Axios, 9 mar. 2023. https://www.axios.com/2023/03/09/algorithmic-bias-meta-dataset-face-recognition

Bellamy, Rachel K. E., et al. 2018. “AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias”. https://arxiv.org/abs/1810.01943

Brasil. 2020. Projeto de Lei nº 2.630/2020. https://www25.senado.leg.br/web/atividade/materias/-/materia/141944.

Brasil. 2023. Projeto de Lei n° 2.338/2023. https:// www25.senado.leg.br/web/atividade/materias/-/materia/157233

Brasil. 2024a. Lei nº 16.321/2024. https://leismunicipais.com.br/a/pr/c/curitiba/lei-ordinaria/2024/1633/16321

Brasil. 2024b. PL nº 2.970/2024. https://mail.camara.rj.gov.br/APL/Legislativos/scpro.nsf/ab87ae0e15e7dddd0325863200569395/03258c16006f052703258ae6006571b4?OpenDocument

Brasil. 2024c. Projeto de Lei nº 210. https://legis.senado.leg.br/sdleg-getter/documento?disposition=inline&dm=9543976&ts=1738768205705

Brasil. 2025. Instrução Normativa PGM nº 003/2025. https://dopaonlineupload.procempa.com.br/dopaonlineupload/5567_ce_20250228_executivo.pdf

Brisola, A.; Bezerra, A. C. 2018. “Desinformação e Circulação de Fake News: distinções, diagnóstico e reação”. Encontro Nacional de Pesquisa em Ciência da Informação – ENANCIB – XIX. Ed. ANCIB, pp. 3316–3330. https://hdl.handle.net/20.500.11959/brapci/102819

Cabrera, Ángel Alexander, et al. 2019. “FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning”. https://arxiv.org/abs/1904.05419 DOI: https://doi.org/10.1109/VAST47406.2019.8986948

Cooper, F. R. 2014. “Always Already Suspect: Revising Vulnerability Theory”. North Carolina Law Review 93.

Doneda, Danilo, e Almeida, Virgílio A. F. (2016). “What Is Algorithm Governance?”. *Internet Governance*, IEEE Computer Society, 1089-7801. DOI: https://doi.org/10.1109/MIC.2016.79

De Mello, F. C. O., e Scheinder, M. (2021). “Desinformação Digital em Rede e Competência Crítica em Informação”. *International Review of Information Ethics*, Vol. 30. DOI: https://doi.org/10.29173/irie408

Deutsch, William et al. (2015). “FairTest: Discovering Unwarranted Associations in Data-Driven Applications”. Disponível em: https://arxiv.org/abs/1510.02377. Acesso em: 02 out. 2025.

Dignum, Virginia. 2019. Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way. Cham: Springer. DOI: https://doi.org/10.1007/978-3-030-30371-6

Errey-Hammond, M. 2024. Big Data, Emerging Technologies and Intelligence. Abingdon: Routledge.

Floridi, Luciano; Cowls, Josh. 2019. “A unified framework of five principles for AI in society”. Harvard Data Science Review, Cambridge, v. 1, n. 1. https://doi.org/10.1162/99608f92.8cd550d1 DOI: https://doi.org/10.1162/99608f92.8cd550d1

Ford, C.; Vogel, K. 2025. Will AI Save Us?: The Role of Artificial Intelligence in Intelligence Analysis. In: US Intelligence Failure and Knowledge Creation: Improving Intelligence Analysis. Abingdon: Routledge. DOI: https://doi.org/10.4324/9781003472926

Gillespie, Tarleton. (2018). *Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions That Shape Social Media*. New Haven: Yale University Press. DOI: https://doi.org/10.12987/9780300235029

Issar, S., e Aneesh, A. (2021). “What is Algorithmic Governance?”. Wiley. DOI: https://doi.org/10.1111/soc4.12955

Iwan-Sojka, D. (2025). “The inclusive data governance models for algorithms”. *Information, Communication & Society*, v. 29, n. 1, p. 1–20. Disponível em: https://www.tandfonline.com/doi/full/10.1080/13600834.2024.2406668. Acesso em: 23 ago. 2025.

Junqueira, A. H., e Botelho-Francisco, R. (2021). “Raça: Dimensão Interseccional das Vulnerabilidades Digitais”. *Contemporanea – Revista de Comunicação e Cultura*, v. 19, n. 3. DOI: https://doi.org/10.9771/contemporanea.v19i3.45899

Katzenbach, C., e Ulbricht, L. (2019). “Algorithmic Governance”. *Internet Policy Review – Journal on Internet Regulation*, v. 8, n. 4. DOI: https://doi.org/10.14763/2019.4.1424

Lovatto, M. Betiele Aude. (2024). “Inteligência Artificial: Governança e Transparência?”. *Revista Ibmec de Direito*, 1(1). Disponível em: https://ibmec.periodicoscientificos.com.br/index.php/cienciajuridica/article/view/245. Acesso em: 05 out. 2025.

Luna, F. (2009). “Elucidating the Concept of Vulnerability: Layers Not Labels”. *The International Journal of Feminist Approaches to Bioethics (Spring)*, Vol. 2, No. 1. DOI: https://doi.org/10.3138/ijfab.2.1.121

Lyon, D. (2014). “Surveillance, Snowden, and Big Data: Capacities, Consequences, Critique”. *Big Data & Society*, 1(2). Disponível em: https://journals.sagepub.com/doi/10.1177/2053951714541861. Acesso em: 23 ago. 2025. DOI: https://doi.org/10.1177/2053951714541861

Malgieri, G., e Niklas, J. (2020). “Vulnerable Data Subjects”. *Computer Law & Security Review*, 37. Elsevier. DOI: https://doi.org/10.1016/j.clsr.2020.105415

Mishra, Neha et al. (2022). “D-BIAS: Human-in-the-Loop Debiasing for Tabular Data Using Causal Models”. Disponível em: https://arxiv.org/abs/2208.05126. Acesso em: 05 out. 2025.

Müller-Birn, C., Herbsleb, J., e Dobusch, L. (2013). “Work-to-Rule: The Emergence of Algorithmic Governance in Wikipedia”. *Proceedings of the 6th International Conference on Communities and Technologies*, 80–89. Disponível em: https://dl.acm.org/doi/10.1145/2482991.2482999. Acesso em: 23 ago. 2025. DOI: https://doi.org/10.1145/2482991.2482999

O’Neil, Cathy. 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Crown.

O’Reilly, T. (2013). “Open Data and Algorithmic Regulation”. Em B. Goldstein & L. Dyson (eds.), *Beyond Transparency: Open Data and the Future of Civic Innovation* (pp. 289–300). San Francisco: Code for America Press.

Pereira Filho, Nivanildo, e Lima, Rogerio de Araujo. (2024). “Governança Algorítmica e Políticas Públicas: Desafios Éticos e Impactos da Inteligência Artificial na Tomada de Decisão Governamental”. *RECIMA21 - Revista Científica Multidisciplinar*, v. 6, n. 1, p. e616051. DOI: 10.47820/recima21.v6i1.6051. Disponível em: https://recima21.com.br/recima21/article/view/6051. Acesso em: 05 out. 2025. DOI: https://doi.org/10.47820/recima21.v6i1.6051

Posetti, J., e Bontcheva, K. (2020). “Desinfodemia: descifrando la desinformación sobre el COVID-19”. Paris: UNESCO. Disponível em: https://en.unesco.org/sites/default/files/disinfodemic_deciphering_covid19_disinformation_es.pdf. Acesso em: 12 set. 2025.

Rouvroy, A., e Berns, T. (2013). “Gouvernementalité algorithmique et perspectives d’émancipation. Le disparate comme condition d’individuation par la relation?”. *Réseaux*, n. 177, p. 163–196. Disponível em: https://www.cairn.info/revue-reseaux-2013-1-page-163.htm. Acesso em: 24 ago. 2025. DOI: https://doi.org/10.3917/res.177.0163

Sandrini, Camila Taciana, e Pereira, Camila Gourgues. (2025). “Governança de Inteligência Artificial no Brasil: Panorama Regulatório Atual”. *Ciências Sociais Aplicadas*, v. 29, n. 149. Disponível em: https://doi.org/10.69849/revistaft/ni10202508072123. Acesso em: 05 out. 2025. DOI: https://doi.org/10.69849/revistaft/ni10202508072123

Stenslie, S. (ed.). 2022. Intelligence Analysis in the Digital Age. Abingdon: Routledge. DOI: https://doi.org/10.4324/9781003168157

Volkoff, Vladimir. (2004). *Pequena História da Desinformação: Do Cavalo de Tróia à Internet*. Curitiba: Ed. Vila do Príncipe.

Yeung, Karen. 2018. “Algorithmic regulation: A critical interrogation”. Regulation & Governance, Hoboken, v. 12, n. 4, p. 505–523. DOI: https://doi.org/10.1111/rego.12158 DOI: https://doi.org/10.1111/rego.12158

Wachter, S., e Mittelstadt, B. (2019). “A Right to Reasonable Inferences: Re-thinking Data Protection Law in the Age of Big Data and AI”. *Columbia Business Law Review*. DOI: https://doi.org/10.31228/osf.io/mu2kf