Challenges in Addressing the Ethical Aspects of Artificial Intelligence to Detect Fraud in Public Procurement Processes
Resumo
Public Procurement Processes (PPPs) involve substantial taxpayer money, necessitating efficiency and transparency. Artificial Intelligence (AI) is increasingly applied to fraud detection in PPPs, enhancing these processes. This work presents a literature review on AI’s role in PPP fraud detection, focusing on ethical and technical challenges, including fairness, transparency, and privacy. We examine the global state of AI applications in PPPs, highlighting best practices and case studies. By analyzing these technologies’ challenges and opportunities, we provide insights and propose strategies for mitigating risks, contributing to the debate on responsible AI adoption in the public sector.
Palavras-chave:
Artificial Intelligence, Public Procurement Processes, Fraud Detection
Referências
Brandão, M. A., Reis, A. P., Mendes, B. M., De Almeida, C. A. B., Oliveira, G. P., Hott, H., Gomide, L. D., Costa, L. L., Silva, M. O., Lacerda, A., et al. (2024). Plus: A semiautomated pipeline for fraud detection in public bids. Digital Government: Research and Practice, 5(1):1–16.
Diaz, J. M. (2017). A taxonomy of corruption in eu public procurement. European Procurement & Public Private Partnership Law Review, 12(4):383–395.
Ezeji, C. L. (2024). Artificial intelligence for detecting and preventing procurement fraud. International Journal of Business Ecosystem & Strategy (2687-2293), 6(1):63–73.
Fazio, D. (2022). Rethinking Discretion in Public Procurement. SSRN.
Ferguson, G. (2018). Global corruption: Law, theory & practice. University of Victoria. 3rd ed. Available at https://canlii.ca/t/27td.
Mohd, S. and Nohuddin, P. N. (2021). A framework of procurement analytics for fraud coalition prediction in malaysia. In 2021 6th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE), volume 6, pages 1–6. IEEE.
Nai, R., Fatima, I., Morina, G., Sulis, E., Genga, L., Meo, R., Pasteris, P., et al. (2023). AI applied to the analysis of the contracts of the italian public administrations. In Proceedings of the Italia Intelligenza Artificiale-Thematic Workshops co-located with the 3rd CINI National Lab AIIS Conference on Artificial Intelligence (Ital IA 2023), pages 255–260. CEUR.
Soylu, A., Corcho, Ó., Elvesæter, B., Badenes-Olmedo, C., Yedro-Martínez, F., Kovacic, M., Posinkovic, M., Medvescek, M., Makgill, I., Taggart, C., et al. (2022). Data quality barriers for transparency in public procurement. Information, 13(2):99.
Torres-Berru, Y., Lopez-Batista, V. F., and Zhingre, L. C. (2023). A data mining approach to detecting bias and favoritism in public procurement. Intelligent Automation & Soft Computing, 36(3).
Velasco, R. B., Carpanese, I., Interian, R., Paulo Neto, O. C., and Ribeiro, C. C. (2021). A decision support system for fraud detection in public procurement. International Transactions in Operational Research, 28(1):27–47.
Diaz, J. M. (2017). A taxonomy of corruption in eu public procurement. European Procurement & Public Private Partnership Law Review, 12(4):383–395.
Ezeji, C. L. (2024). Artificial intelligence for detecting and preventing procurement fraud. International Journal of Business Ecosystem & Strategy (2687-2293), 6(1):63–73.
Fazio, D. (2022). Rethinking Discretion in Public Procurement. SSRN.
Ferguson, G. (2018). Global corruption: Law, theory & practice. University of Victoria. 3rd ed. Available at https://canlii.ca/t/27td.
Mohd, S. and Nohuddin, P. N. (2021). A framework of procurement analytics for fraud coalition prediction in malaysia. In 2021 6th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE), volume 6, pages 1–6. IEEE.
Nai, R., Fatima, I., Morina, G., Sulis, E., Genga, L., Meo, R., Pasteris, P., et al. (2023). AI applied to the analysis of the contracts of the italian public administrations. In Proceedings of the Italia Intelligenza Artificiale-Thematic Workshops co-located with the 3rd CINI National Lab AIIS Conference on Artificial Intelligence (Ital IA 2023), pages 255–260. CEUR.
Soylu, A., Corcho, Ó., Elvesæter, B., Badenes-Olmedo, C., Yedro-Martínez, F., Kovacic, M., Posinkovic, M., Medvescek, M., Makgill, I., Taggart, C., et al. (2022). Data quality barriers for transparency in public procurement. Information, 13(2):99.
Torres-Berru, Y., Lopez-Batista, V. F., and Zhingre, L. C. (2023). A data mining approach to detecting bias and favoritism in public procurement. Intelligent Automation & Soft Computing, 36(3).
Velasco, R. B., Carpanese, I., Interian, R., Paulo Neto, O. C., and Ribeiro, C. C. (2021). A decision support system for fraud detection in public procurement. International Transactions in Operational Research, 28(1):27–47.
Publicado
27/11/2024
Como Citar
SAMPAIO, Igor Garcia Ballhausen; BERNARDINI, Flávia Cristina; VITERBO, José.
Challenges in Addressing the Ethical Aspects of Artificial Intelligence to Detect Fraud in Public Procurement Processes. In: CONFERÊNCIA LATINO-AMERICANA DE ÉTICA EM INTELIGÊNCIA ARTIFICIAL, 1. , 2024, Niteroi.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 13-16.
DOI: https://doi.org/10.5753/laai-ethics.2024.32440.
