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English Information

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Authors
# Name
1 Dimas Nascimento(dimas.cassimiro@ufape.edu.br)
2 Daliton Silva(daliton.silva@ufape.edu.br)
3 Luis Pereira(luis-filipe.pereira@ufape.edu.br)

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Reference
# Reference
1 Agarwal, S. and Mishra, S. (2021). Responsible AI. Springer
2 Barocas, S. and Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3):671–732.
3 eldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., and Venkatasubramanian, S. (2015). Certifying and removing disparate impact. In proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pages 259–268.
4 Fernando, M.-P., C` esar, F., David, N., and Jos´e, H.-O. (2021). Missing the missing values: The ugly duckling of fairness in machine learning. International Journal of Intelligent Systems, 36(7):3217–3258.
5 Little, R. J. and Rubin, D. B. (2019). Statistical Analysis with Missing Data. John Wiley & Sons.
6 Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM computing surveys (CSUR), 54(6):1–35.
7 Mitchell, S. e. a. (2021). Algorithmic fairness: Choices, assumptions, and definitions. Communications of the ACM, 64(5):58–66.
8 Oliveira, T. A., Oliveira, J. V., Farias, T. P., Cruz, E. W., Andrade, L. J., and Pita, R. (2024). Estudo experimental sobre justiça algorítmica aplicada em modelos de análise de crédito. In Simpósio Brasileiro de Banco de Dados (SBBD), pages 29–36. SBC.
9 Wang, Y. and Singh, L. (2021). Analyzing the impact of missing values and selection bias on fairness. International Journal of Data Science and Analytics, 12(2):101–119.
10 Zafar, M. B., Valera, I., Rogriguez, M. G., and Gummadi, K. P. (2017). Fairness constraints: Mechanisms for fair classification. In Artificial intelligence and statistics, pages 962–970. PMLR.