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English Information

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Authors
# Name
1 Tiago Almeida(oliveira.t@ufba.br)
2 João Victor Luz(j.luz@ufba.br)
3 Tarcísio Paraíso(tarcisioparaiso@gmail.com)
4 Erick Welber Reis(erickwellber@gmail.com)
5 Leandro José Silva(leojsandrade@gmail.com)
6 Robespierre Dantas da Rocha(pierre.pita@gmail.com)

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Reference
# Reference
1 Akkizidis, I. and Stagars, M. (2015). Marketplace lending, Financial Analysis, and the Future of credit: Integration, Profitability, and risk management. John Wiley & Sons
2 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.
3 Bellamy, R. K., Hind, M., Mojsilovi´c, A., et al. (2019). Ai fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias. IBM JRD, 63(4/5):4–1.
4 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.
5 Caton, S. and Haas, C. (2024). Fairness in machine learning: A survey. ACM Computing Surveys, 56(7):1–38.
6 Derelio˘glu, G., G¨urgen, F., and Okay, N. (2009). A neural approach for sme’s credit risk analysis in turkey. pages 749–759. Springer
7 Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R. (2012). Fairness through awareness. In Proceedings of the 3rd ITCS, pages 214–226.
8 Guidolin, M. and Pedio, M. (2021). Sharpening the accuracy of credit scoring models withmachine learning algorithms. In Data Science for Economics and Finance: Methodologies and Applications, pages 89–115. Springer
9 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.
10 Kozodoi, N., Jacob, J., and Lessmann, S. (2022). Fairness in credit scoring: Assessment, implementation and profit implications. EJOR, 297(3):1083–1094