Artificial Intelligence in the Promotion of Justice, Equality, and Efficiency in Public Procurement and Contracting

Authors

  • Jônata Tyska Carvalho Federal University of Santa Catarina
  • Márcio Castro Federal University of Santa Catarina

DOI:

https://doi.org/10.5753/compbr.2026.56.8610

Keywords:

Artificial Intelligence, public procurement, fraud prevention, transparency, administrative efficiency

Abstract

With the advancement of digitalization in the public sector, Brazil has started to generate a vast amount of data on contracts, procurement processes, payments, and suppliers. In this context, Artificial Intelligence emerges as a powerful ally in making public procurement fairer, more equitable, and more efficient. By integrating different databases, the use of Artificial Intelligence technologies makes it possible to identify suspicious patterns, such as companies lacking operational capacity, abnormal pricing, and possible fraud networks. More than simply automating audits, AI helps strengthen oversight, reduce waste, and protect fair competition. The challenge, however, lies in building reliable, standardized, and up-to-date databases. Tools such as Large Language Models (LLMs) expand the ability to organize information contained in texts and documents, but they require rigorous evaluation to avoid errors and biases. In this process, partnerships between public agencies and universities play a central role, and Brazil already has promising initiatives showing how technology can help combat irregularities and improve public management.

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References

DA SILVA, Hudson AB et al. Uma Abordagem para a Gestão da Linhagem de Dados Heterogêneos. In: Simpósio Brasileiro de Banco de Dados (SBBD). SBC, 2025. p. 630-643. DOI: 10.5753/sbbd.2025.247293

BECKHAUSER, William Jones; FILETO, Renato. EchoRAG: a framework for enhancing language models with graph-RAG and in-context learning. Machine Learning, v. 114, n. 10, p. 215, 2025. DOI: 10.1007/s10994-025-06859-1

BECKHAUSER, William; FILETO, Renato. Boosting not-so-large language models by using knowledge graphs and reinforcement learning. In: Proceedings of the 15th Brazilian Symposium in Information and Human Language Technology. 2024. p. 115-124. DOI: 10.5753/stil.2024.245396.

SCHNEIDER DOS SANTOS, Everton et al. Detection of fraud in public procurement using data-driven methods: a systematic mapping study. EPJ Data Science, v. 14, n. 1, p. 52, 2025. DOI: 10.1140/epjds/s13688-025-00569-3

MINISTÉRIO PÚBLICO DO ESTADO DE MINAS GERAIS (MPMG). MPMG recebe comitiva do Centro Integrado de Inteligência da Região Sudeste para troca de experiências. Portal do Ministério Público do Estado de Minas Gerais, 10 nov. 2021. Disponível em: [link]. Acesso em: 19 fev. 2026.

CÉOS. Inteligência Artificial em benefício da sociedade. CÉOS, s. d. Disponível em: [link]. Acesso em: 19 fev. 2026.

RIO GRANDE DO SUL. Governo do Estado. Parceria do governo com a USP ampliará alcance de tecnologia que precifica produtos. Portal do Estado do Rio Grande do Sul, 13 fev. 2025. Disponível em: [link]. Acesso em: 19 fev. 2026.

UNIVERSIDADE FEDERAL DO RIO GRANDE DO NORTE (UFRN). UFRN e TCU firmam parceria na área de auditoria do SUS. Portal da UFRN, 9 fev. 2026. Disponível em: [link]. Acesso em: 19 fev. 2026.

Published

2026-07-01

How to Cite

Carvalho, J. T., & Castro, M. (2026). Artificial Intelligence in the Promotion of Justice, Equality, and Efficiency in Public Procurement and Contracting. Brazil Computing, (56), 25–28. https://doi.org/10.5753/compbr.2026.56.8610

Issue

Section

Papers