Racismo Algorítmico em Sistemas de Inteligência Artificial: Uma Revisão Sistemática da Literatura
Resumo
Este artigo apresenta uma revisão sistemática da literatura sobre racismo algorítmico em sistemas de Inteligência Artificial (IA), analisando seus impactos sociais. A pesquisa, baseada no protocolo de Kitchenham e Charters (2007), analisou 38 estudos fundamentaram as respostas às questões de pesquisa. Os resultados indicam que o racismo algorítmico se manifesta em diferentes contextos, como saúde, segurança pública e plataformas digitais, sendo influenciado por dados enviesados e fatores institucionais. Observam-se impactos como discriminação e ampliação de desigualdades, evidenciando a necessidade de abordagens técnicas, éticas e regulatórias para mitigação.Referências
ABHARI, J.; ASHOK, A. (2023). Mitigating Racial Biases for Machine Learning Based Skin Cancer Detection. In: International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc '23), 24. New York: ACM, p. 556–561. DOI: 10.1145/3565287.3617639.
ALLAREDDY, V. et al. (2023). Call for algorithmic fairness to mitigate amplification of racial biases in artificial intelligence models used in orthodontics and craniofacial health. Orthodontics & Craniofacial Research, v. 26, s. 1, p. 124–130. DOI: 10.1111/ocr.12721.
BAEZA-YATES, R. (2022). Ethical Challenges in AI. In: ACM International Conference on Web Search and Data Mining, 15. New York: ACM, p. 1–2. DOI: 10.1145/3488560.3498370.
BARDIN, L. (2011) Análise de conteúdo. Tradução de Luís Antero Reto e Augusto Pinheiro. São Paulo: Edições 70. Tradução de: L’Analyse de Contenu, 1997. Disponível em: [link]. Acesso em: set. 2025.
BLACK, E.; YEOM, S.; FREDRIKSON, M. (2020). FlipTest: fairness testing via optimal transport. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20). New York: ACM, p. 111–121. DOI: 10.1145/3351095.3372845.
BROWNE, S. (2010). “Digital Epidermalization: Race, Identity and Biometrics”. Critical Sociology, v. 36, n. 1, p. 131–150.
BUOLAMWINI, J.; GEBRU, T. (2018). “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification”. In: Conference on Fairness, Accountability and Transparency. p. 77–91.
COE, J.; ATAY, M. (2021). Evaluating Impact of Race in Facial Recognition across Machine Learning and Deep Learning Algorithms. Computers, v. 10, n. 9, p. 113. DOI: 10.3390/computers10090113.
CHEN, Z. et al. (2024). Fairness Testing: A Comprehensive Survey and Analysis of Trends. ACM Transactions on Software Engineering and Methodology, v. 33, n. 5, p. 1–59. DOI: 10.1145/3652155.
GALHOTRA, S.; BRUN, Y.; MELIOU, A. (2017). Fairness testing: testing software for discrimination. In: Joint Meeting on Foundations of Software Engineering, 11. New York: ACM, p. 498–510. DOI: 10.1145/3106237.3106277.
GRABOWICZ, P. A.; PERELLO, N.; MISHRA, A. (2022). Marrying Fairness and Explainability in Supervised Learning. In: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT '22). New York: ACM, p. 1905–1916. DOI: 10.1145/3531146.3533236.
HAIMSON, O. L. et al. (2025). AI Attitudes Among Marginalized Populations in the U.S.: Nonbinary, Transgender, and Disabled Individuals Report More Negative AI Attitudes. In: ACM Conference on Fairness, Accountability, and Transparency (FAccT '25). New York: ACM, p. 1224–1237. DOI: 10.1145/3715275.3732081.
HAQUE, M. R. et al. (2024). Are We Asking the Right Questions?: Designing for Community Stakeholders’ Interactions with AI in Policing. In: CHI Conference on Human Factors in Computing Systems (CHI '24). New York: ACM, p. 1–20. Art. 301. DOI: 10.1145/3613904.3642738.
HALE, J.; KIM, P. H.; GRATCH, J. (2025). Algoritmos “provavelmente justos” podem perpetuar o viés racial e de gênero: um estudo sobre a resolução de disputas salariais. Autonomous Agents and Multi-Agent Systems, v. 39, n. 20.
HAMID, T. et al. (2025). DermaGlow: Objective Quantification of Melanin, Erythema and Skin-tone Using Wearable Optical Spectroscopy. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, v. 9, n. 2, p. 1–28. DOI: 10.1145/3729474.
HAN, J. X. et al. (2024). A Causal Framework To Evaluate Racial Bias in Law Enforcement Systems. In: AAAI/ACM Conference on AI, Ethics, and Society, 7. [S.l.: s.n.].
HARRINGTON, C. N. et al. (2022). “It’s Kind of Like Code-Switching”: Black Older Adults’ Experiences with a Voice Assistant for Health Information Seeking. In: CHI Conference on Human Factors in Computing Systems (CHI '22). New York: ACM, p. 1–15. Art. 604. DOI: 10.1145/3491102.3501995.
IMANA, B.; KOROLOVA, A.; HEIDEMANN, J. (2025). Auditing for Bias in Ad Delivery Using Inferred Demographic Attributes. In: ACM Conference on Fairness, Accountability, and Transparency (FAccT '25). New York: ACM, p. 2640–2656. DOI: 10.1145/3715275.3732172.
INTAHCHOMPHOO, C.; GUNDERSEN, O. E. (2020). “Artificial Intelligence and Race: a Systematic Review”. Legal Information Management, v. 20, n. 2, p. 74–84. DOI: 10.1017/S1472669620000183.
JIN, Z. et al. (2020). MithraCoverage: A System for Investigating Population Bias for Intersectional Fairness. In: ACM SIGMOD International Conference on Management of Data (SIGMOD '20). New York: ACM, p. 2721–2724. DOI: 10.1145/3318464.3384689.
JOHNSON, D. K. N. (2025). Gaslighting Ourselves: Racial Challenges of Artificial Intelligence in Economics and Finance Applications. The Review of Black Political Economy, v. 52, n. 3, p. 303–335. DOI: 10.1177/00346446251331615.
KHADEMI, A. et al. (2019). Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality. In: The World Wide Web Conference (WWW '19). New York: ACM, p. 2907–2914. DOI: 10.1145/3308558.3313559.
KITCHENHAM, B. A.; CHARTERS, S. (2007) Guidelines for performing systematic literature reviews in software engineering. Tech. Rep. EBSE-2007-01, Keele University.
KOSTICK-QUENET, K. M.; COHEN, I. G.; GERKE, S. et al. Mitigating Racial Bias in Machine Learning. Journal of Law, Medicine & Ethics, v. 50, n. 1, p. 92–100, 2022. DOI: 10.1017/jme.2022.13.
LI, J.; MOSKOVITCH, Y.; JAGADISH, H. V. (2021). DENOUNCER: detection of unfairness in classifiers. Proceedings of the VLDB Endowment, v. 14, n. 12, p. 2719–2722. DOI: 10.14778/3476311.3476328.
MARKS, P. (2021). Can the biases in facial recognition be fixed; also, should they? Communications of the ACM, v. 64, n. 3, p. 20–22. DOI: 10.1145/3446877.
METAXA, D. et al. (2021). An Image of Society: Gender and Racial Representation and Impact in Image Search Results for Occupations.
MUNANGA, Kabengele. Rediscutindo a mestiçagem no Brasil: identidade nacional versus identidade negra. Petrópolis, RJ: Vozes, 1999.
NOBLE, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. New York: NYU Press.
NYLAND, J. J. A. O. L. (2023). “Racismo algorítmico: uma revisão de literature”. Research, Society and Development, v. 12, n. 2, e1912239907. DOI: 10.33448/rsd-v12i2.39907.
OBERMEYER, Z. et al. (2019). “Dissecting racial bias in an algorithm used to manage the health of populations”. Science, v. 366, n. 6464, p. 447–453.
O'NEIL, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Crown.
ORPHANOU, K.; OTTERBACHER, J.; KLEANTHOUS, S.; BATSUREN, K.; GIUNCHIGLIA, F.; BOGINA, V.; SHULNER TAL, A.; HARTMAN, A.; KUFLIK, T. (2023). Mitigating Bias in Algorithmic Systems—A Fish-eye View. ACM Computing Surveys, v. 55, n. 5, Art. 87, p. 1–37. DOI: 10.1145/3527152.
PÄÄKKÖNEN, J. et al. (2020). Bureaucracy as a Lens for Analyzing and Designing Algorithmic Systems. In: CHI Conference on Human Factors in Computing Systems (CHI '20). New York: ACM, p. 1–14. DOI: 10.1145/3313831.3376780.
PERES, I. E. V. et al. (2021). “Preconceito em algoritmos de aprendizagem de máquina e suas bases de treinamento: uma revisão sistemática de literatura”. Universidade Presbiteriana Mackenzie, São Paulo.
PUYOL-ANTÓN, E. et al. (2022). Fairness in Cardiac Magnetic Resonance Imaging: Assessing Sex and Racial Bias in Deep Learning-Based Segmentation. Frontiers in Cardiovascular Medicine, v. 9. DOI: 10.3389/fcvm.2022.859310.
SAPIEZYNSKI, P. et al. (2022). Algorithms that “Don’t See Color”: Measuring Biases in Lookalike and Special Ad Audiences. In: AAAI/ACM Conference on AI, Ethics, and Society (AIES '22). New York: ACM, p. 609–616. DOI: 10.1145/3514094.3534135.
SHAHID, S. B. et al. (2025). Bias in Deep Learning Skin Cancer Detection: Parallel Residual Convolution Network Classification and Racial Bias Quantification: Skin Cancer Racial Bias. In: International Conference on Computing Advancements, 3. New York: ACM, p. 993–1000. DOI: 10.1145/3723178.3723310.
SILVA, T. (2022). Racismo algorítmico: inteligência artificial e discriminação nas redes digitais. São Paulo: Editora Polis.
SMITH, J. M. (2024). “I'm Sorry, but I Can't Assist”: Bias in Generative AI. In: RESPECT Annual Conference (RESPECT 2024). New York: ACM, p. 75–80. DOI: 10.1145/3653666.3656065.
SRINIVASAN, R.; CHANDER, A. (2021). Biases in AI Systems: A Survey for Practitioners. Queue, v. 19, n. 2, p. 1–20. DOI: 10.1145/3466132.3466134.
TANKSLEY, T. et al. (2025). “Ethics is not neutral”: Understanding Ethical and Responsible AI Design from the Lenses of Black Youth. In: CHI Conference on Human Factors in Computing Systems (CHI '25). New York: ACM, p. 1–20. Art. 200. DOI: 10.1145/3706598.3713510.
WÓJCIK, M. A. (2023). Assessing the Legality of Using the Category of Race and Ethnicity in Clinical Algorithms: the EU Anti-Discrimination Law Perspective. In: EWAF.
YANG, K. et al. (2018). A Nutritional Label for Rankings. In: International Conference on Management of Data (SIGMOD '18). New York: ACM, p. 1773–1776. DOI: 10.1145/3183713.3193568.
YUCER, S. et al. (2025). Racial Bias within Face Recognition: A Survey. ACM Computing Surveys, v. 57, n. 4, p. 1–39. Art. 105. DOI: 10.1145/3705295.
ZHANG, L.; ZHANG, Y.; ZHANG, M. (2021). Efficient white-box fairness testing through gradient search. In: Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2021). New York: ACM, p. 103–114. DOI: 10.1145/3460319.3464820.
ZHAO, D. et al. (2021). Understanding and Evaluating Racial Biases in Image Captioning. In: IEEE/CVF International Conference on Computer Vision (ICCV), Montreal. [S.l.]: IEEE, p. 14810–14820. DOI: 10.1109/ICCV48922.2021.01456.
ALLAREDDY, V. et al. (2023). Call for algorithmic fairness to mitigate amplification of racial biases in artificial intelligence models used in orthodontics and craniofacial health. Orthodontics & Craniofacial Research, v. 26, s. 1, p. 124–130. DOI: 10.1111/ocr.12721.
BAEZA-YATES, R. (2022). Ethical Challenges in AI. In: ACM International Conference on Web Search and Data Mining, 15. New York: ACM, p. 1–2. DOI: 10.1145/3488560.3498370.
BARDIN, L. (2011) Análise de conteúdo. Tradução de Luís Antero Reto e Augusto Pinheiro. São Paulo: Edições 70. Tradução de: L’Analyse de Contenu, 1997. Disponível em: [link]. Acesso em: set. 2025.
BLACK, E.; YEOM, S.; FREDRIKSON, M. (2020). FlipTest: fairness testing via optimal transport. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20). New York: ACM, p. 111–121. DOI: 10.1145/3351095.3372845.
BROWNE, S. (2010). “Digital Epidermalization: Race, Identity and Biometrics”. Critical Sociology, v. 36, n. 1, p. 131–150.
BUOLAMWINI, J.; GEBRU, T. (2018). “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification”. In: Conference on Fairness, Accountability and Transparency. p. 77–91.
COE, J.; ATAY, M. (2021). Evaluating Impact of Race in Facial Recognition across Machine Learning and Deep Learning Algorithms. Computers, v. 10, n. 9, p. 113. DOI: 10.3390/computers10090113.
CHEN, Z. et al. (2024). Fairness Testing: A Comprehensive Survey and Analysis of Trends. ACM Transactions on Software Engineering and Methodology, v. 33, n. 5, p. 1–59. DOI: 10.1145/3652155.
GALHOTRA, S.; BRUN, Y.; MELIOU, A. (2017). Fairness testing: testing software for discrimination. In: Joint Meeting on Foundations of Software Engineering, 11. New York: ACM, p. 498–510. DOI: 10.1145/3106237.3106277.
GRABOWICZ, P. A.; PERELLO, N.; MISHRA, A. (2022). Marrying Fairness and Explainability in Supervised Learning. In: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT '22). New York: ACM, p. 1905–1916. DOI: 10.1145/3531146.3533236.
HAIMSON, O. L. et al. (2025). AI Attitudes Among Marginalized Populations in the U.S.: Nonbinary, Transgender, and Disabled Individuals Report More Negative AI Attitudes. In: ACM Conference on Fairness, Accountability, and Transparency (FAccT '25). New York: ACM, p. 1224–1237. DOI: 10.1145/3715275.3732081.
HAQUE, M. R. et al. (2024). Are We Asking the Right Questions?: Designing for Community Stakeholders’ Interactions with AI in Policing. In: CHI Conference on Human Factors in Computing Systems (CHI '24). New York: ACM, p. 1–20. Art. 301. DOI: 10.1145/3613904.3642738.
HALE, J.; KIM, P. H.; GRATCH, J. (2025). Algoritmos “provavelmente justos” podem perpetuar o viés racial e de gênero: um estudo sobre a resolução de disputas salariais. Autonomous Agents and Multi-Agent Systems, v. 39, n. 20.
HAMID, T. et al. (2025). DermaGlow: Objective Quantification of Melanin, Erythema and Skin-tone Using Wearable Optical Spectroscopy. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, v. 9, n. 2, p. 1–28. DOI: 10.1145/3729474.
HAN, J. X. et al. (2024). A Causal Framework To Evaluate Racial Bias in Law Enforcement Systems. In: AAAI/ACM Conference on AI, Ethics, and Society, 7. [S.l.: s.n.].
HARRINGTON, C. N. et al. (2022). “It’s Kind of Like Code-Switching”: Black Older Adults’ Experiences with a Voice Assistant for Health Information Seeking. In: CHI Conference on Human Factors in Computing Systems (CHI '22). New York: ACM, p. 1–15. Art. 604. DOI: 10.1145/3491102.3501995.
IMANA, B.; KOROLOVA, A.; HEIDEMANN, J. (2025). Auditing for Bias in Ad Delivery Using Inferred Demographic Attributes. In: ACM Conference on Fairness, Accountability, and Transparency (FAccT '25). New York: ACM, p. 2640–2656. DOI: 10.1145/3715275.3732172.
INTAHCHOMPHOO, C.; GUNDERSEN, O. E. (2020). “Artificial Intelligence and Race: a Systematic Review”. Legal Information Management, v. 20, n. 2, p. 74–84. DOI: 10.1017/S1472669620000183.
JIN, Z. et al. (2020). MithraCoverage: A System for Investigating Population Bias for Intersectional Fairness. In: ACM SIGMOD International Conference on Management of Data (SIGMOD '20). New York: ACM, p. 2721–2724. DOI: 10.1145/3318464.3384689.
JOHNSON, D. K. N. (2025). Gaslighting Ourselves: Racial Challenges of Artificial Intelligence in Economics and Finance Applications. The Review of Black Political Economy, v. 52, n. 3, p. 303–335. DOI: 10.1177/00346446251331615.
KHADEMI, A. et al. (2019). Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality. In: The World Wide Web Conference (WWW '19). New York: ACM, p. 2907–2914. DOI: 10.1145/3308558.3313559.
KITCHENHAM, B. A.; CHARTERS, S. (2007) Guidelines for performing systematic literature reviews in software engineering. Tech. Rep. EBSE-2007-01, Keele University.
KOSTICK-QUENET, K. M.; COHEN, I. G.; GERKE, S. et al. Mitigating Racial Bias in Machine Learning. Journal of Law, Medicine & Ethics, v. 50, n. 1, p. 92–100, 2022. DOI: 10.1017/jme.2022.13.
LI, J.; MOSKOVITCH, Y.; JAGADISH, H. V. (2021). DENOUNCER: detection of unfairness in classifiers. Proceedings of the VLDB Endowment, v. 14, n. 12, p. 2719–2722. DOI: 10.14778/3476311.3476328.
MARKS, P. (2021). Can the biases in facial recognition be fixed; also, should they? Communications of the ACM, v. 64, n. 3, p. 20–22. DOI: 10.1145/3446877.
METAXA, D. et al. (2021). An Image of Society: Gender and Racial Representation and Impact in Image Search Results for Occupations.
MUNANGA, Kabengele. Rediscutindo a mestiçagem no Brasil: identidade nacional versus identidade negra. Petrópolis, RJ: Vozes, 1999.
NOBLE, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. New York: NYU Press.
NYLAND, J. J. A. O. L. (2023). “Racismo algorítmico: uma revisão de literature”. Research, Society and Development, v. 12, n. 2, e1912239907. DOI: 10.33448/rsd-v12i2.39907.
OBERMEYER, Z. et al. (2019). “Dissecting racial bias in an algorithm used to manage the health of populations”. Science, v. 366, n. 6464, p. 447–453.
O'NEIL, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Crown.
ORPHANOU, K.; OTTERBACHER, J.; KLEANTHOUS, S.; BATSUREN, K.; GIUNCHIGLIA, F.; BOGINA, V.; SHULNER TAL, A.; HARTMAN, A.; KUFLIK, T. (2023). Mitigating Bias in Algorithmic Systems—A Fish-eye View. ACM Computing Surveys, v. 55, n. 5, Art. 87, p. 1–37. DOI: 10.1145/3527152.
PÄÄKKÖNEN, J. et al. (2020). Bureaucracy as a Lens for Analyzing and Designing Algorithmic Systems. In: CHI Conference on Human Factors in Computing Systems (CHI '20). New York: ACM, p. 1–14. DOI: 10.1145/3313831.3376780.
PERES, I. E. V. et al. (2021). “Preconceito em algoritmos de aprendizagem de máquina e suas bases de treinamento: uma revisão sistemática de literatura”. Universidade Presbiteriana Mackenzie, São Paulo.
PUYOL-ANTÓN, E. et al. (2022). Fairness in Cardiac Magnetic Resonance Imaging: Assessing Sex and Racial Bias in Deep Learning-Based Segmentation. Frontiers in Cardiovascular Medicine, v. 9. DOI: 10.3389/fcvm.2022.859310.
SAPIEZYNSKI, P. et al. (2022). Algorithms that “Don’t See Color”: Measuring Biases in Lookalike and Special Ad Audiences. In: AAAI/ACM Conference on AI, Ethics, and Society (AIES '22). New York: ACM, p. 609–616. DOI: 10.1145/3514094.3534135.
SHAHID, S. B. et al. (2025). Bias in Deep Learning Skin Cancer Detection: Parallel Residual Convolution Network Classification and Racial Bias Quantification: Skin Cancer Racial Bias. In: International Conference on Computing Advancements, 3. New York: ACM, p. 993–1000. DOI: 10.1145/3723178.3723310.
SILVA, T. (2022). Racismo algorítmico: inteligência artificial e discriminação nas redes digitais. São Paulo: Editora Polis.
SMITH, J. M. (2024). “I'm Sorry, but I Can't Assist”: Bias in Generative AI. In: RESPECT Annual Conference (RESPECT 2024). New York: ACM, p. 75–80. DOI: 10.1145/3653666.3656065.
SRINIVASAN, R.; CHANDER, A. (2021). Biases in AI Systems: A Survey for Practitioners. Queue, v. 19, n. 2, p. 1–20. DOI: 10.1145/3466132.3466134.
TANKSLEY, T. et al. (2025). “Ethics is not neutral”: Understanding Ethical and Responsible AI Design from the Lenses of Black Youth. In: CHI Conference on Human Factors in Computing Systems (CHI '25). New York: ACM, p. 1–20. Art. 200. DOI: 10.1145/3706598.3713510.
WÓJCIK, M. A. (2023). Assessing the Legality of Using the Category of Race and Ethnicity in Clinical Algorithms: the EU Anti-Discrimination Law Perspective. In: EWAF.
YANG, K. et al. (2018). A Nutritional Label for Rankings. In: International Conference on Management of Data (SIGMOD '18). New York: ACM, p. 1773–1776. DOI: 10.1145/3183713.3193568.
YUCER, S. et al. (2025). Racial Bias within Face Recognition: A Survey. ACM Computing Surveys, v. 57, n. 4, p. 1–39. Art. 105. DOI: 10.1145/3705295.
ZHANG, L.; ZHANG, Y.; ZHANG, M. (2021). Efficient white-box fairness testing through gradient search. In: Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2021). New York: ACM, p. 103–114. DOI: 10.1145/3460319.3464820.
ZHAO, D. et al. (2021). Understanding and Evaluating Racial Biases in Image Captioning. In: IEEE/CVF International Conference on Computer Vision (ICCV), Montreal. [S.l.]: IEEE, p. 14810–14820. DOI: 10.1109/ICCV48922.2021.01456.
Publicado
19/07/2026
Como Citar
CAMPOS, Adriele Mesquita de Souza; SANTOS, Natanael da Silva dos; SILVA, Douglas Tadeu Andrade da; CUNHA, Libia de Souza Boss.
Racismo Algorítmico em Sistemas de Inteligência Artificial: Uma Revisão Sistemática da Literatura. In: WORKSHOP SOBRE AS IMPLICAÇÕES DA COMPUTAÇÃO NA SOCIEDADE (WICS), 7. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 288-302.
ISSN 2763-8707.
DOI: https://doi.org/10.5753/wics.2026.23828.
