Inteligência artificial como apoio à tomada de decisão no setor público
DOI:
https://doi.org/10.59490/dgo.2025.997Keywords:
Processo Decisório, Informação, Tratamento de dados, Gestão de processos, GovernançaAbstract
O artigo apresenta uma Revisão Sistemática da Literatura sobre a adoção de soluções de inteligência artificial (IA) como ferramenta de apoio à tomada de decisão na administração pública, destacando como essa tecnologia pode contribuir para modernizar os processos de gestão governamental. O levantamento de 38 artigos sobre inteligência artificial aplicada à tomada de decisão destaca sua versatilidade em diversas áreas, incluindo governança, serviços sociais, saúde, meio ambiente, justiça e orçamento público. Na governança, enfatizam-se a transparência, a explicabilidade e a supervisão humana, essenciais para decisões automatizadas confiáveis. Serviços sociais enfrentam desafios éticos, como impactos no bem-estar dos trabalhadores e desumanização do atendimento. Na saúde, IA otimiza cadeias de suprimentos e resposta a crises, enquanto na justiça, melhora a análise de dados e a prevenção de crimes. Na operação de sistemas de energia e em sistemas ambientais o foco está na eficiência e precisão. Embora a inteligência artificial tenha um enorme potencial para transformar a gestão pública, seu uso requer uma análise criteriosa de questões éticas, governança e transparência. Problemas como viés algorítmico e ausência de supervisão reforçam a importância de regulamentações adequadas e de diferentes abordagens que integrem a tecnologia com a supervisão humana.
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Alon-Barkat, S., & Busuioc, M. (2022). Human-AI interactions in public sector decision-making: “Automation bias” and “selective adherence” to algorithmic advice. Journal of Public Administration Research and Theory, 33(1), 153–169. https://doi.org/10.1093/jopart/muac007
Ayat Mohammad Salem, Serife Zihni Eyupoglu, & Mohammad Khaleel Ma’aitah. (2024). The Influence of Machine Learning on Enhancing Rational Decision-Making and Trust Levels in e-Government. Systems, 12(9), 373–373. https://doi.org/10.3390/systems12090373
Ban, Y. (2020). Energy decision making of steel company based on energy management system. IFAC-PapersOnLine, 53(5), 608–613. https://doi.org/10.1016/j.ifacol.2021.04.151.
Bardin, L. Análise de conteúdo. Lisboa: Edições 70, 2010.
Bertoncini, C., Brito, A., Leme, E., Silva, I., Silva, T., Ferreira, Perri, R., Alves, A., & Autônomo. (n.d.). PROCESSO DECISÓRIO: A TOMADA DE DECISÃO. Retrieved February 9, 2025, from [link]
Bokhari, S. A. A., & Myeong, S. (2022). Use of Artificial Intelligence in Smart Cities for Smart Decision-Making: A Social Innovation Perspective. Sustainability, 14(2), 620. https://doi.org/10.3390/su14020620
Bokhari, S. A. A., & Myeong, S. (2023). The Impact of AI Applications on Smart Decision-making in Smart Cities as Mediated by the Internet of Things and Smart Governance. IEEE Access, 11, 120827–120844. https://doi.org/10.1109/access.2023.3327174
Brasil. Ministério da Ciência, Tecnologia e Inovações. Plano Brasileiro de Inteligência Artificial (PBIA) 2024. Laboratório Nacio nal de Computação Científica - LNCC, 7 ago. 2024. Disponível em: [link] cias-1/plano-brasileiro-de-inteligencia-artificial-pbia-2024-2028.
Brynjolfsson, E., & Mcafee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. (2025). Google Books. [link]
Choi, W., Joonyeop Na, & Lee, S. (2024). Evaluating Intelligent CPTED Systems to Support Crime Prevention Decision-Making in Municipal Control Centers. Applied Sciences, 14(15), 6581–6581. https://doi.org/10.3390/app14156581
Choi, Y., Gil-Garcia, R., Aranay, O., Burke, B., & Werthmuller, D. (2021). Using Artificial Intelligence Techniques for Evidence-Based Decision Making in Government: Random Forest and Deep Neural Network Classification for Predicting Harmful Algal Blooms in New York State. DG.O2021: The 22nd Annual International Conference on Digital Government Research, 27–37. https://doi.org/10.1145/3463677.3463713
de Bruijn, H., Warnier, M., & Janssen, M. (2021). The perils and pitfalls of explainable AI: Strategies for explaining algorithmic decision-making. Government Information Quarterly, 39(2), 101666. https://doi.org/10.1016/j.giq.2021.101666
Sousa, W. G. de, Melo, E. R. P. de, Bermejo, P. H. D. S., Farias, R. A. S., & Gomes, A. O. (2019). How and where is artificial intelligence in the public sector going? A literature review and research agenda. Government Information Quarterly, 36(4), 101392. https://doi.org/10.1016/j.giq.2019.07.004.
Denhardt, J. V., & Denhardt, R. B. (2015). The New Public Service Revisited. Public Administration Review, 75(5), 664–672. https://doi.org/10.1111/puar.12347
Deveci, M. (2023). Effective use of artificial intelligence in healthcare supply chain resilience using fuzzy decision-making model. Soft Computing. https://doi.org/10.1007/s00500-023-08906-2
Di Vaio, A., Hassan, R., & Alavoine, C. (2022). Data intelligence and analytics: A bibliometric analysis of human–Artificial intelligence in public sector decision-making effectiveness. Technological Forecasting and Social Change, 174, 121201. https://doi.org/10.1016/j.techfore.2021.121201
Erick, & Antônio, M. (2024). A IMPORTÂNCIA DA TOMADA DE DECISÕES NA GESTÃO EMPRESARIAL. Contemporânea, 4(3), e3663–e3663. https://doi.org/10.56083/rcv4n3-122
Fischer-Abaigar, U., Kern, C., Barda, N., & Kreuter, F. (2024). Bridging the gap: Towards an expanded toolkit for AI-driven decision-making in the public sector. Government Information Quarterly, 41(4), 101976. https://doi.org/10.1016/j.giq.2024.101976
Frost, N. (2024). The Impoverished Publicness of Algorithmic Decision Making. Oxford Journal of Legal Studies. https://doi.org/10.1093/ojls/gqae027
Gelashvili, T., & Pappel, I. (2021, July 1). Challenges of Transition to Paperless Management: Readiness of Incorporating AI in Decision-making processes. IEEE Xplore. https://doi.org/10.1109/ICEDEG52154.2021.9530905
Grimmelikhuijsen, S. (2023). Explaining why the computer says no: Algorithmic transparency affects the perceived trustworthiness of automated decision-making. Public Administration Review, 83(1), 12-25. https://doi.org/10.1111/puar.13483.
Governo Federal do Brasil. (2024). Plano Brasileiro de Inteligência Artificial (PBIA) 2024-2028. Brasília, DF: Ministério da Ciência, Tecnologia e Inovação. Recuperado de [link].
Guo, X., Chen, P., Liang, S., Jiao, Z., Li, L., Yan, J., Huang, Y., Liu, Y., & Fan, W. (2022). PaCAR: COVID-19 Pandemic Control Decision Making via Large-Scale Agent-Based Modeling and Deep Reinforcement Learning. Medical Decision Making, 0272989X2211079. https://doi.org/10.1177/0272989x221107902
Hanne Hirvonen. (2023). Just accountability structures – a way to promote the safe use of automated decision-making in the public sector. AI & SOCIETY. https://doi.org/10.1007/s00146-023-01731-z
Ingrams, A., Kaufmann, W., & Jacobs, D. (2021). In AI we trust? Citizen perceptions of AI in government decision making. Policy & Internet. https://doi.org/10.1002/poi3.276
Janssen, M., Hartog, M., Matheus, R., Yi Ding, A., & Kuk, G. (2020). Will Algorithms Blind People? The Effect of Explainable AI and Decision-Makers’ Experience on AI-supported Decision-Making in Government. Social Science Computer Review, 40(2), 089443932098011. https://doi.org/10.1177/0894439320980118
Kolkman, D., Bex, F., Narayan, N., & van. (2024). Justitia ex machina: The impact of an AI system on legal decision-making and discretionary authority. Big Data & Society, 11(2). https://doi.org/10.1177/20539517241255101
Kuziemski, M., & Misuraca, G. (2020). AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings. Telecommunications Policy, 44(6), 101976. https://doi.org/10.1016/j.telpol.2020.101976
Liu, H. K., Tang, M., & Chen, K.-H. (2020). Public Decision Making. 214–222. https://doi.org/10.1145/3396956.3396965
Liu, B., Luo, J., & Su, X. (2021). The Framework of 6G Self-Evolving Networks and the Decision-Making Scheme for Massive IoT. Applied Sciences, 11(19), 9353. https://doi.org/10.3390/app11199353
Matthias Döring, Kim Sass Mikkelsen, Jonas Krogh Madsen, & Kristian Bloch Haug. (2024). Creating a workforce of fatigued cynics? A randomized controlled trial of implementing an algorithmic decision-making support tool. Government Information Quarterly, 41(1), 101911–101911. https://doi.org/10.1016/j.giq.2024.101911
Maximiano, A. C. A. (2000). Introdução à administração (5. ed.). São Paulo: Atlas.
Mills, D., Pudney, S., Pevcin, P., & Dvorak, J. (2021). Evidence-Based Public Policy Decision-Making in Smart Cities: Does Extant Theory Support Achievement of City Sustainability Objectives? Sustainability, 14(1), 3. https://doi.org/10.3390/su14010003
Monarcha-Matlak, A. (2021). Automated decision-making in public administration. Procedia Computer Science, 192, 2077–2084. https://doi.org/10.1016/j.procs.2021.08.215
Mourby, M. J. (2021). “Leading by Science” through Covid-19: the GDPR & Automated Decision-Making. International Journal of Population Data Science, 5(4). https://doi.org/10.23889/ijpds.v5i4.1402
O’Reilly, T. (2011). Government as a Platform. Innovations: Technology, Governance, Globalization, 6(1), 13–40. https://doi.org/10.1162/inov_a_00056
Pereira, F., Silva, L., Souza, L., et al. (2021). A transformação digital no setor público: Desafios e oportunidades da inteligência artificial. Revista de Administração Pública, 55(3), 451-470.
PRISMA. (2020). Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA). Www.prisma-Statement.org. [link]
Ranerup, A., & Henriksen, H. Z. (2020). Digital Discretion: Unpacking Human and Technological Agency in Automated Decision Making in Sweden’s Social Services. Social Science Computer Review, 40(2), 089443932098043. https://doi.org/10.1177/0894439320980434
Rinta-Kahila, T., Someh, I., Gillespie, N., Indulska, M., & Gregor, S. (2021). Algorithmic decision-making and system destructiveness: A case of automatic debt recovery. European Journal of Information Systems, 31(3), 1–26. https://doi.org/10.1080/0960085x.2021.1960905
Sampaio, R. C., Sabbatini, M., & Limongi, R. (2024). Diretrizes para o uso ético e responsável da inteligência artificial generativa: Um guia prático para pesquisadores. São Paulo: Editora Intercom.
Seyedzadeh, S., Pour Rahimian, F., Oliver, S., Rodriguez, S., & Glesk, I. (2020). Machine learning modelling for predicting non-domestic buildings energy performance: A model to support deep energy retrofit decision-making. Applied Energy, 279, 115908. https://doi.org/10.1016/j.apenergy.2020.115908
Silva, W. F. da, Silva, F. S., & Rabêlo, O. D. S. (2021). Tendências no Uso de Inteligência Artificial e sua Influência na Requalificação da Força de Trabalho no Setor Público. Cadernos de Prospecção, 14(3), 824. https://doi.org/10.9771/cp.v14i3.36727
Simon, H. A. (1997). Administrative behavior: A study of decision-making processes in administrative organizations (4. ed.). New York: Free Press.
Silva, W. F. da, Silva, F. S., & Rabêlo, O. D. S. (2021). Tendências no Uso de Inteligência Artificial e sua Influência na Requalificação da Força de Trabalho no Setor Público. Cadernos de Prospecção, 14(3), 824. https://doi.org/10.9771/cp.v14i3.36727
Sleep, L. (2024). “This is NOT human services”: Counter-mapping automated decision-making in social services in Australia. Journal of Sociology, 60(3), 618–642. https://doi.org/10.1177/14407833241266022
Tayeb, A., Alzubi, A., & Iyiola, K. (2024). Artificial intelligence and smart decision making in smart cities: a parallel moderated mediation approach. International Journal of Urban Sciences, 1–29. https://doi.org/10.1080/12265934.2024.2407796
Tran Thi Hoang, G., Dupont, L., & Camargo, M. (2019). Application of Decision-Making Methods in Smart City Projects: A Systematic Literature Review. Smart Cities, 2(3), 433–452. https://doi.org/10.3390/smartcities2030027
Valle-Cruz, D., Fernandez-Cortez, V., & Gil-Garcia, J. R. (2021). From E-budgeting to smart budgeting: Exploring the potential of artificial intelligence in government decision-making for resource allocation. Government Information Quarterly, 39(2), 101644. https://doi.org/10.1016/j.giq.2021.101644
Virmani, N., Rajesh Kumar Singh, Agarwal, V., & Emel Aktas. (2024). Artificial Intelligence Applications for Responsive Healthcare Supply Chains: A Decision-Making Framework. IEEE Transactions on Engineering Management, 1–41. https://doi.org/10.1109/tem.2024.3370377
Wang, S., Min, C., Liang, Z., Zhang, Y., & Gao, Q. (2024). The decision-making by citizens: Evaluating the effects of rule-driven and learning-driven automated responders on citizen-initiated contact. Computers in Human Behavior, 161, 108413. https://doi.org/10.1016/j.chb.2024.108413
Warthon, M. (2024). Restricting access to AI decision-making in the public interest: The justificatory role of proportionality and its balancing factors. Internet Policy Review, 13(3). https://doi.org/10.14763/2024.3.1801
Wilson, C., & van der Velden, M. (2022). Sustainable AI: An integrated model to guide public sector decision-making. Technology in Society, 68, 101926. https://doi.org/10.1016/j.techsoc.2022.101926
Zabihi, O., Siamaki, M., Gheibi, M., Akrami, M., & Hajiaghaei-Keshteli, M. (2023). A smart sustainable system for flood damage management with the application of artificial intelligence and multi-criteria decision-making computations. International Journal of Disaster Risk Reduction, 84, 103470. https://doi.org/10.1016/j.ijdrr.2022.103470
Zuiderwijk, A., Chen, Y.-C., & Salem, F. (2021). Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda. Government Information Quarterly, 38(3), 101577. Sciencedirect. https://doi.org/10.1016/j.giq.2021.101577
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Copyright (c) 2025 Camila Taciana Sandrini, Taiane Ritta Coelho, Milton Cesar Adriao, Edelvino Razzolini Filho
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