The Logic of Forgetting

power, data governance, and the decolonial critique of artificial intelligence

Authors

DOI:

https://doi.org/10.46230/lef.v17i3.16015

Keywords:

artificial intelligence, algorithmic bias, social invisibility, algorithmic justice

Abstract

The expansion of Artificial Intelligence (AI) into critical domains like healthcare and security raises urgent concerns about equity and justice. This article argues that algorithmic biases are not mere technical errors but manifestations of historical structural inequalities, reflected in datasets that function as sociotechnical artifacts. The research investigates two central mechanisms of exclusion: distorted representation, which includes groups in stereotyped ways, and selective invisibility, which systematically erases entire populations from records. Using a theoretical framework that articulates Critical Algorithm Studies, Data Feminism, and the Decolonial critique, we analyze an analytical mosaic of emblematic cases, including facial recognition, automated recruitment, and the omission of LGBTQIA+ data. We demonstrate the existence of a feedback loop of inequality, arguing that purely technical 'debiasing' solutions are insufficient, often masking the political choices embedded within them. Finally, the paper proposes structural guidelines for algorithmic justice, focusing on data sovereignty, effective contestability, and democratic governance. 

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Author Biographies

Jefferson Igor Duarte Silva, Instituto Federal do Rio Grande do Norte (IFRN)

Possui graduação em Redes de Computadores pelo Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Norte (2013) e Mestrado em Sistemas e Computação pela UFRN (PPgsC/UFRN). Atualmente é Professor na área de Redes de Computadores do Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Norte. Tem experiência na área de Ciência da Computação, com ênfase em Indústria 4.0 e aprendizado de máquina. 

Akynara Aglaé Rodrigues Santos da Silva Burlamaqui, Universidade Federal Rural do Semi-Árido (UFERSA)

Professora Adjunta da Universidade Federal Rural do Semi-Árido (UFERSA, Centro Multidisciplinar de Angicos). Professora do quadro permanente do Programa de Pós-Graduação em Inovação em Tecnologias Educacionais (IMD- UFRN). Coordenadora do Laboratório de Tecnologias da Informação e Comunicação Aplicadas ao Contexto Educacional (LATIC - UFERSA, Angicos). Líder do grupo de Estudos e Pesquisa em Educação, Inovação, Tecnologias e Acessibilidade (EITA).Coordenadora de área do Programa Institucional de Bolsas de Iniciação à Docência (Pibid) - LCI, UFERSA, Angicos. Vice coordenadora do Curso de Pedagogia (UFERSA, Angicos). Co-fundadora da RoboEduc, Escola de Programação e Robótica, Natal, RN. Participa como pesquisadora do Laboratório de Tecnologias Educacionais, Assistivas e Multimídia (Npiti, IMD, UFRN). Participa de projetos de pesquisa / extensão envolvendo Psicologia Educacional, Aprendizagem Criativa, Tecnologias Educacionais, Formação e Prática Docente.

Aquiles Medeiros Filgueira Burlamaqui, Universidade Federal do Rio Grande do Norte (UFRN)

Possui Graduação em Ciências da Computação pela UFRN(Universidade Federal do Rio Grande do Norte) em 2003, Mestrado em Sistemas e Computação pela UFRN (2004). Doutor em Engenharia da Computação pela UFRN. Atualmente é professor Associado da UFRN, atuando na graduação na ECT(Escola de Ciências e Tecnologia) e na pós-graduação na ECT e IMD(Instituto Metrópole Digital). É pesquisador e coordenador nos laboratórios LAR(Laboratório de Automação e Robótica) e TEAM(Laboratório de Tecnologias Educacionais, Assistivas e Multimídia), atuando principalmente nos seguintes temas: Metaverso, Ambientes Virtuais Colaborativos Massivos, TV Digital Interativa, Sistemas Web, Aplicações de Robótica, Realidade Virtual, Engenharia de Software e Arte Telemática. Vem atuando fortemente na formação de pessoas para atuarem em ambientes de Inovação e Empreendedorismo. Já participou da formação de mais de 10 startups, incluindo a Roboeduc e Faceponto.

References

AJALA, F.; NYERERE, I. Digital Harvests, Digital Shadows: the risks of data colonialism in african smallholder agriculture. Nairobi: African Centre for Technology Studies, 2022.

AMOORE, L. Cloud Ethics: algorithms and the attributes of ourselves and others. Durham: Duke University Press, 2020. DOI: https://doi.org/10.1515/9781478009276

BAROCAS, S.; HARDT, M.; NARAYANAN, A. Fairness and Machine Learning: limitations and opportunities. Cambridge: MIT Press, 2019. Disponível em: https://fairmlbook.org. Acesso em: 22 out. 2025.

BENJAMIN, R. Race After Technology: abolitionist tools for the new Jim Code. Cambridge: Polity Press, 2019.

BIRHANE, A. Algorithmic Colonization of Africa. SCRIPTed, v. 17, n. 2, p. 389-405, 2020. Disponível em: https://script-ed.org/article/algorithmic-colonization-of-africa/. Acesso em: 23 out. 2025. DOI: https://doi.org/10.2966/scrip.170220.389

BOLUKBASI, T.; CHANG, K-W.; ZOU, J.; SALIGRAMA V.; KALAI A. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. In: Conference On Neural Information Processing Systems, 30., 2016, Barcelona. p. 4349–4357, 2016. Disponível em: https://papers.nips.cc/paper/2016/hash/a486cd07e4ac3d270571622f4f316ec5-Abstract.html. Acesso em: 22 out. 2025.

BUOLAMWINI, J.; GEBRU, T. Gender Shades: intersectional accuracy disparities in commercial gender classification. In: Conference On Fairness, Accountability, And Transparency, 2018, New York. Proceedings [...]. Cambridge: PMLR, 2018. v. 81, p. 77-91.

CARROLL, S. R.; GARBA, I.; FIGUEROA-RODRÍGUEZ, O. L.; HOLBROOK, J.; LOVETT, R.; MATERECHERA, S.; PARSONS, M.; RASEROKA, K.; RODRIGUEZ-LONEBEAR, D.; ROWE, R.; SARA, R.; WALKER, J. D.; ANDERSON, J.; HUDSON, M. The CARE principles for indigenous data governance. Data science journal, v. 19, p. 2-12 2020. Disponível em: https://datascience.codata.org/articles/1158/files/submission/proof/1158-1-8528-2-10-20201104.pdf. Acesso em: 28 out. 2025.

CARVALHO, A. A. de; BARRETO, R. C. V. A invisibilidade das pessoas LGBTQIA+ nas bases de dados: novas possibilidades na Pesquisa Nacional de Saúde 2019?. Ciência & Saúde Coletiva, v. 26, p. 4059-4064, 2021. Disponível em: https://www.scielo.br/j/csc/a/rwDkNhDCdyY5xdfyXNxmmGH/?format=html&lang=pt. Acesso em: 28 out. 2025. DOI: https://doi.org/10.1590/1413-81232021269.12002021

CITRON, D. K. Technological due process. Wash. UL Rev., v. 85, p. 1249, 2007. Disponível em: https://openscholarship.wustl.edu/cgi/viewcontent.cgi?article=1166&context=law_lawreview. Acesso em: 28 out. 2025.

COSTANZA-CHOCK, S. Design justice: community-led practices to build the worlds we need. The MIT Press, 2020. DOI: https://doi.org/10.7551/mitpress/12255.001.0001

COULDRY, N.; MEJIAS, U. A. The Costs of Connection: how data is colonizing human life and appropriating it for capitalism. Stanford: Stanford University Press, 2019. DOI: https://doi.org/10.1515/9781503609754

CRAWFORD, K. Atlas of AI: power, politics, and the planetary costs of artificial intelligence. New Haven: Yale University Press, 2021. DOI: https://doi.org/10.12987/9780300252392

DASTIN, J. Amazon scraps secret AI recruiting tool that showed bias against women. Reuters, 10 out. 2018. Disponível em: https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G. Acesso em: 7 jul. 2025.

DE LIMA VIANA, G. M.; SPERANDEO DE MACEDO, C. Inteligência artificial e a discriminação algorítmica: : uma análise do caso amazon. Direito & TI, [S. l.], v. 1, n. 19, p. 39–62, 2024. DOI: 10.63451/ti.v1i19.212. Disponível em: https://www.direitoeti.com.br/direitoeti/article/view/212. Acesso em: 4 nov. 2025. DOI: https://doi.org/10.63451/ti.v1i19.212

DENIS, C.; ELIE R.; HEBIRI, M.; HU F. Fairness guarantees in multi-class classification with demographic parity. Journal of Machine Learning Research, v. 25, n. 130, p. 1- 46, 2024. Disponível em: https://arxiv.org/abs/2109.13642. Acesso em: 28 out. 2025.

D'IGNAZIO, C.; KLEIN, L. F. Data Feminism. Cambridge: The MIT Press, 2020. DOI: https://doi.org/10.7551/mitpress/11805.001.0001

FERRETTI, V.; MONTIBELLER, G.; VON WINTERFELDT, D. Testing the effectiveness of debiasing techniques to reduce overprecision in the elicitation of subjective continuous probability distributions. European Journal of Operational Research, v. 304, n. 2, p. 661-675, 2023. Disponível em: https://www.sciencedirect.com/science/article/pii/S0377221722003046. Acesso em 28 out. 2025. DOI: https://doi.org/10.1016/j.ejor.2022.04.008

FJELD, J.; ACHTEN, N.; HILLIGOSS, H.; NAGY, A. C.; SRIKUMAR, M. Principled artificial intelligence: Mapping consensus in ethical and rights-based approaches to principles for AI. Berkman Klein Center Research Publication, n. 2020-1, 2020. Disponível em: https://www.researchgate.net/publication/339138141_Principled_Artificial_Intelligence_Mapping_Consensus_in_Ethical_and_Rights-Based_Approaches_to_Principles_for_AI. Acesso em: 28 out. 2025. DOI: https://doi.org/10.2139/ssrn.3518482

GEBRU, T.; MORGENSTERN, J.; VECCHIONE B.; VAUGHAN, J. W. WALLACH, H.; DAUMÉ III, H.; CRAWFORD, K. Datasheets for Datasets. Communications of the ACM, New York, v. 64, n. 12, p. 86–92, dez. 2021. Disponível em: https://arxiv.org/abs/1803.09010. Acesso em: 28 out. 2025. DOI: https://doi.org/10.1145/3458723

GUPTA, N.; MUJUMDAR, S.; PATEL, H.; MASUDA, S.; PANWAR, N.; BANDYOPADHYAY, S.; MEHTA, S.; GUTTULA, S.; AFZAL, S.; MITTAL, R. S.; MUNIGALA, V. Data quality for machine learning tasks. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining. The ACM Digital Library. KDD '21, p. 4040-4041. 2021. Disponível em: https://dl.acm.org/doi/abs/10.1145/3447548.3470817. Acesso em: 30 out. 2025. DOI: https://doi.org/10.1145/3447548.3470817

HARDT, M.; PRICE, E.; SREBRO, N. Equality of opportunity in supervised learning. In: Proceedings of the 30th International Conference on Neural Information Processing Systems. v. 29, p. 3323–3331, 2016. Disponível em: https://dl.acm.org/doi/abs/10.5555/3157382.3157469. Acesso em: 30 out. 2025.

HATZENBUEHLER, M. L; LATTANNER, M. R; MCKETTA, S.; PACHANKIS, J. E. Structural stigma and LGBTQ+ health: a narrative review of quantitative studies. The Lancet Public Health, [S.L.], v. 9, n. 2, p. 109-127, fev. 2024. Elsevier BV. http://dx.doi.org/10.1016/s2468-2667(23)00312-2. Disponível em: https://www.thelancet.com/action/showPdf?pii=S2468-2667%2823%2900312-2. Acesso em: 04 nov. 2025. YIN, R. K. Case Study Research and Applications: design and methods. 6. ed. Thousand Oaks: Sage, 2018. DOI: https://doi.org/10.1016/S2468-2667(23)00312-2

HEEKS, R.; RENKEN, J. Data justice for development: What would it mean? Information Development, v. 34, n. 1, p. 90-102, 2018. Disponível em: https://journals.sagepub.com/doi/abs/10.1177/0266666916678282. Acesso em: 30 out. 2025. DOI: https://doi.org/10.1177/0266666916678282

IYER, N.; ACHIENG, G.; BOROKINI, F.; LUDGER, U. Automated imperialism, expansionist dreams: exploring digital extractivism in Africa. POLLICY, 2021. Disponível em: https://archive.pollicy.org/wp-content/uploads/2021/06/Automated-Imperialism-Expansionist-Dreams-Exploring-Digital-Extractivism-in-Africa.pdf. Acesso em: 30 out. 2025.

MBEMBE, A. Necropolítica. São Paulo: n-1 Edições, 2019.

MEHRABI, N.; MORSTATTER, F.; SAXENA, N.; LERMAN, K.; GALSTYAN, A. A survey on bias and fairness in machine learning. ACM computing surveys (CSUR), v. 54, n. 6, p. 1-35, 2021. Disponível em: https://dl.acm.org/doi/10.1145/3457607. Acesso em: 30 out. 2025. DOI: https://doi.org/10.1145/3457607

MITCHELL, M.; WU, S.; ZALDIVAR, A.; BARNES, P.; VASSERMAN, L.; HUTCHINSON, B.; SPITZER, E.; RAJI, I. D.; GEBRU, T. Model Cards for Model Reporting. In: Conference On Fairness, Accountability, And Transparency, 2019, Atlanta. Proceedings [...]. New York: ACM, 2019. p. 220–229. Disponível em: https://arxiv.org/abs/1810.03993. Acesso em: 30 out. 2025. DOI: https://doi.org/10.1145/3287560.3287596

NOBLE, S. U. Algorithms of Oppression: how search engines reinforce racism. New York: New York University Press, 2018. DOI: https://doi.org/10.18574/nyu/9781479833641.001.0001

ORR, W.; CRAWFORD, K. The social construction of datasets: on the practices, processes, and challenges of dataset creation for machine learning. New Media & Society, v. 26, n. 9, p. 4955-4972, 2024. DOI: https://doi.org/10.1177/14614448241251797

PASQUALE, F. The Black Box Society: the secret algorithms that control money and information. Cambridge: Harvard University Press, 2015. DOI: https://doi.org/10.4159/harvard.9780674736061

QUIJANO, A. A colonialidade do saber: eurocentrismo e ciências sociais. Perspectivas latino-americanas. Buenos Aires: CLACSO, p. 117-142, 2005. Disponível em: https://ufrb.edu.br/educacaodocampocfp/images/Edgardo-Lander-org-A-Colonialidade-do-Saber-eurocentrismo-e-ciC3AAncias-sociais-perspectivas-latinoamericanas-LIVRO.pdf. Acesso em: 30 out. 2025.

RAJI, I. D.; BUOLAMWINI, J. Actionable Auditing: investigating the impact of publicly naming biased performance results of commercial AI products. In: AAAI/ACM Conference On Ai, Ethics, And Society, 2019, Honolulu. Proceedings [...]. New York: ACM, 2019. p. 429-435. Disponível em: https://www.media.mit.edu/publications/actionable-auditing-investigating-the-impact-of-publicly-naming-biased-performance-results-of-commercial-ai-products/. Acesso em: 30 out. 2025. DOI: https://doi.org/10.1145/3306618.3314244

RAMIRO, A.; CRUZ, L. The grey-zones of public-private surveillance: Policy tendencies of facial recognition for public security in Brazilian cities. Internet Policy Review, v. 12, n. 1. p. 1-28, 2023. Disponível em: https://policyreview.info/pdf/policyreview-2023-1-1705.pdf. Acesso em: 30 out. 2025. DOI: https://doi.org/10.14763/2023.1.1705

REDE de Observatórios Da Segurança. Pele Alvo: a cor da violência policial. [S.l.]: Rede de Observatórios da Segurança, 2021. Disponível em: https://redeobservatorios.com.br/wp-content/uploads/2022/08/Pele-Alvo-A-cor-da-violencia-policial.pdf. Acesso em: 7 jul. 2025.

ROCHE, C.; WALL, P. J.; LEWIS, D. Ethics and diversity in artificial intelligence policies, strategies and initiatives. AI and Ethics, v. 3, n. 4, p. 1095-1115, 2023. Disponível em: https://pubmed.ncbi.nlm.nih.gov/36246014/. Acesso em: 30 out. 2025. DOI: https://doi.org/10.1007/s43681-022-00218-9

ROSA, A. M. da; GUASQUE, B. Inteligência artificial, vieses algorítmicos e racismo: o lado desconhecido da justiça algorítmica. Opinión Jurídica, v. 23, n. 50, p. 1-23, 2024. Disponível em: http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S1692-25302024000200008. Acesso em: 30 out. 2025. DOI: https://doi.org/10.22395/ojum.v23n50a49

SELBST, A. D. Disparate impact in big data policing. Ga. L. Rev., v. 52, p. 109, 2017. Disponível em: https://dlg.usg.edu/record/ugalaw_glr_vol52-iss1-6. Acesso em: 30 out. 2025.

SCHEUERMAN, M. K.; JIANG, J. A.; FIESLER, C.; BRUBAKER, J. R. ‘What is it about ‘lesbian’ that is so scary?’: How AI-driven content moderation systems fail LGBTQ+ creators. Proceedings of the ACM on Human-Computer Interaction, New York, v. 5, n. CSCW2, art. 422, p. 368:1–35, out. 2021. Disponível em: https://arxiv.org/pdf/2108.04401. Acesso em: 30 out. 2025. DOI: https://doi.org/10.1145/3479512

VERMA, S.; RUBIN, J. Fairness definitions explained. In: IEEE/ACM INTERNATIONAL WORKSHOP ON SOFTWARE FAIRNESS, 2018, Gotemburgo. Proceedings [...]. New York: IEEE, 2018. p. 1-7. Disponível em: https://dl.acm.org/doi/10.1145/3194770.3194776. Acesso em: 01 nov. 2025. DOI: https://doi.org/10.1145/3194770.3194776

WANG, A.; RAMASWAMY, V. V.; RUSSAKOVSKY, O. Towards intersectionality in machine learning: including more identities, handling underrepresentation, and performing evaluation. In: Proceedings of the 2022 ACM conference on fairness, accountability, and transparency. 2022. p. 336-349. Disponível em: https://dl.acm.org/doi/fullHtml/10.1145/3531146.3533101. Acesso em: 01 nov. 2025. DOI: https://doi.org/10.1145/3531146.3533101

YIN, R. K. Case Study Research and Applications: design and methods. 6. ed. Thousand Oaks: Sage, 2018.

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2025-12-10

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SILVA, J. I. D.; BURLAMAQUI, A. A. R. S. da S.; BURLAMAQUI, A. M. F. The Logic of Forgetting: power, data governance, and the decolonial critique of artificial intelligence. Linguagem em Foco Scientific Journal, Fortaleza, v. 17, n. 3, p. 70–90, 2025. DOI: 10.46230/lef.v17i3.16015. Disponível em: https://revistas.uece.br/index.php/linguagememfoco/article/view/16015. Acesso em: 15 dec. 2025.

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Vol. 17 No. 3 (2025): DOSSIÊ TEMÁTICO: Inteligência Artificial, racismo algorítmico e outras exclusões

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Copyright (c) 2025 Jefferson Igor Duarte Silva, Akynara Aglaé Rodrigues Santos da Silva Burlamaqui, Aquiles Medeiros Filgueira Burlamaqui

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