Estudo experimental sobre justiça algorítmica aplicada em modelos de análise de crédito
Resumo
Modelos de Machine Learning (ML) para tomada de decisão algorítmica são amplamente aplicados para suportar a gestão de risco e análise de crédito. Contudo, o sensível aumento de dados disponíveis, a complexidade dos modelos mais modernos e o escrutínio público em torno da inteligência artificial acirraram o debate sobre a necessidade de identificação e mitigação de vieses em predições. Este estudo propõe analisar a relação entre medidas quantitativas de justiça algorítmica e métricas de qualidade obtidas por modelos de ML em tarefas de análise de crédito. Os resultados iniciais indicam que determinados modelos conseguem alcançar níveis promissores de desempenho sem necessariamente afetar ou deteriorar a justiça em suas predições.
Palavras-chave:
justiça algoritmica, machine learning, análise de crédito
Referências
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Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., and Vanthienen, J. (2003). Benchmarking state-of-the-art classification algorithms for credit scoring. JORS), 54(6):627–635.
Bellamy, R. K., Hind, M., Mojsilović, A., et al. (2019). Ai fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias. IBM JRD, 63(4/5):4–1.
Bhatore, S., Mohan, L., and Reddy, Y. R. (2020). Machine learning techniques for credit risk evaluation: a systematic literature review. Journal of Banking and Financial Technology, 4(1):111–138.
Caton, S. and Haas, C. (2024). Fairness in machine learning: A survey. ACM Computing Surveys, 56(7):1–38.
Derelioğlu, G., Gürgen, F., and Okay, N. (2009). A neural approach for sme’s credit risk analysis in turkey. pages 749–759. Springer.
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R. (2012). Fairness through awareness. In Proceedings of the 3rd ITCS, pages 214–226.
Guidolin, M. and Pedio, M. (2021). Sharpening the accuracy of credit scoring models with machine learning algorithms. In Data Science for Economics and Finance: Methodologies and Applications, pages 89–115. Springer.
Gyamfi, N. K. and Abdulai, J.-D. (2018). Bank fraud detection using support vector machine. In 2018 IEEE 9th IEMCON, pages 37–41. IEEE.
Hardt, M., Price, E., Price, E., and Srebro, N. (2016). Equality of opportunity in supervised learning. In Lee, D., editor, NIPS, volume 29. Curran Associates, Inc.
He, Y., Burghardt, K., and Lerman, K. (2020). A geometric solution to fair representations. In Proceedings of the AAAI/ACM, AIES ’20, page 279–285, USA. ACM.
Hofmann, H. (1994). Statlog (german credit data).
Kasmi, M. L. (2021). Machine learning fairness in finance: An application to credit scoring. Diss., Tilburg University.
Kozodoi, N., Jacob, J., and Lessmann, S. (2022). Fairness in credit scoring: Assessment, implementation and profit implications. EJOR, 297(3):1083–1094.
Lai, K. K. and Zhou, L. (2006). Neural network metalearning for credit scoring. In International Conference on Intelligent Computing, pages 403–408. Springer.
Lessmann, S., Baesens, B., Seow, H.-V., and Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. EJOR, 247(1):124–136.
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6):1–35.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. JMLR, 12:2825–2830.
Ruback, L., Carvalho, D., and Avila, S. (2022). Mitigando vieses no aprendizado de máquina: Uma análise sociotécnica. iSys, 15(1):23–1.
Sadok, H., Sakka, F., and El Maknouzi, M. E. H. (2022). Artificial intelligence and bank credit analysis: A review. Cogent Economics, 10(1):2023262.
Sathye, M. et al. (2003). Credit analysis & lending management. Wiley.
Szwabe, A. and Misiorek, P. (2018). Decision trees as interpretable bank credit scoring models. In BDAS 2018, Poland, September 18-20, 2018, Proceedings 14, pages 207–219. Springer.
West, D., Dellana, S., and Qian, J. (2005). Neural network ensemble strategies for financial decision applications. COR, 32(10):2543–2559.
Xia, Y., Liu, C., Li, Y., and Liu, N. (2017). A boosted decision tree approach using bayesian hyper-parameter optimization for credit scoring. Expert systems with applications, 78:225–241.
Zhou, L., Lai, K. K., and Yu, L. (2010). Least squares support vector machines ensemble models for credit scoring. Expert systems with applications, 37(1):127–133.
Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., and Vanthienen, J. (2003). Benchmarking state-of-the-art classification algorithms for credit scoring. JORS), 54(6):627–635.
Bellamy, R. K., Hind, M., Mojsilović, A., et al. (2019). Ai fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias. IBM JRD, 63(4/5):4–1.
Bhatore, S., Mohan, L., and Reddy, Y. R. (2020). Machine learning techniques for credit risk evaluation: a systematic literature review. Journal of Banking and Financial Technology, 4(1):111–138.
Caton, S. and Haas, C. (2024). Fairness in machine learning: A survey. ACM Computing Surveys, 56(7):1–38.
Derelioğlu, G., Gürgen, F., and Okay, N. (2009). A neural approach for sme’s credit risk analysis in turkey. pages 749–759. Springer.
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R. (2012). Fairness through awareness. In Proceedings of the 3rd ITCS, pages 214–226.
Guidolin, M. and Pedio, M. (2021). Sharpening the accuracy of credit scoring models with machine learning algorithms. In Data Science for Economics and Finance: Methodologies and Applications, pages 89–115. Springer.
Gyamfi, N. K. and Abdulai, J.-D. (2018). Bank fraud detection using support vector machine. In 2018 IEEE 9th IEMCON, pages 37–41. IEEE.
Hardt, M., Price, E., Price, E., and Srebro, N. (2016). Equality of opportunity in supervised learning. In Lee, D., editor, NIPS, volume 29. Curran Associates, Inc.
He, Y., Burghardt, K., and Lerman, K. (2020). A geometric solution to fair representations. In Proceedings of the AAAI/ACM, AIES ’20, page 279–285, USA. ACM.
Hofmann, H. (1994). Statlog (german credit data).
Kasmi, M. L. (2021). Machine learning fairness in finance: An application to credit scoring. Diss., Tilburg University.
Kozodoi, N., Jacob, J., and Lessmann, S. (2022). Fairness in credit scoring: Assessment, implementation and profit implications. EJOR, 297(3):1083–1094.
Lai, K. K. and Zhou, L. (2006). Neural network metalearning for credit scoring. In International Conference on Intelligent Computing, pages 403–408. Springer.
Lessmann, S., Baesens, B., Seow, H.-V., and Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. EJOR, 247(1):124–136.
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6):1–35.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. JMLR, 12:2825–2830.
Ruback, L., Carvalho, D., and Avila, S. (2022). Mitigando vieses no aprendizado de máquina: Uma análise sociotécnica. iSys, 15(1):23–1.
Sadok, H., Sakka, F., and El Maknouzi, M. E. H. (2022). Artificial intelligence and bank credit analysis: A review. Cogent Economics, 10(1):2023262.
Sathye, M. et al. (2003). Credit analysis & lending management. Wiley.
Szwabe, A. and Misiorek, P. (2018). Decision trees as interpretable bank credit scoring models. In BDAS 2018, Poland, September 18-20, 2018, Proceedings 14, pages 207–219. Springer.
West, D., Dellana, S., and Qian, J. (2005). Neural network ensemble strategies for financial decision applications. COR, 32(10):2543–2559.
Xia, Y., Liu, C., Li, Y., and Liu, N. (2017). A boosted decision tree approach using bayesian hyper-parameter optimization for credit scoring. Expert systems with applications, 78:225–241.
Zhou, L., Lai, K. K., and Yu, L. (2010). Least squares support vector machines ensemble models for credit scoring. Expert systems with applications, 37(1):127–133.
Publicado
14/10/2024
Como Citar
OLIVEIRA, Tiago A.; OLIVEIRA, João V. L.; FARIAS, Tarcísio P.; CRUZ, Erick W. R.; ANDRADE, Leandro J. S.; PITA, Robespierre.
Estudo experimental sobre justiça algorítmica aplicada em modelos de análise de crédito. In: WORKSHOP DE TRABALHOS DE ALUNOS DA GRADUAÇÃO (WTAG) - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 39. , 2024, Florianópolis/SC.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 29-36.
DOI: https://doi.org/10.5753/sbbd_estendido.2024.243797.
