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Authors
# Name
1 Êrica do Carmo(ericapetersc@usp.br)
2 Agma Traina(agma@icmc.usp.br)
3 Caetano Traina Jr.(caetano@icmc.usp.br)

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Reference
# Reference
1 Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A. V., Al Muhanna, D., and Al-Muhanna, F. A. (2023). A review of the role of artificial intelligence in healthcare. Journal of personalized medicine, 13(6):951.
2 Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D. (2020). Invariant risk minimization. Preprint disponível em: https://arxiv.org/abs/1907.02893v3.
3 Brasil (2018). Lei nº 13.709, de 14 de agosto de 2018. Diário Oficial da União.
4 Chen, R. J., Wang, J. J., Williamson, D. F., Chen, T. Y., Lipkova, J., Lu, M. Y., Sahai, S., and Mahmood, F. (2023). Algorithmic fairness in artificial intelligence for medicine and healthcare. Nature biomedical engineering, 7(6):719–742.
5 Deng, L. (2012). The mnist database of handwritten digit images for machine learning research. IEEE Signal Processing Magazine, 29(6):141–142.
6 Ezzeldin, Y. H. (2023). Fairfed: Enabling group fairness in federated learning. In Proceedings of the AAAI Conference on Artificial Intelligence.
7 Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., et al. (2019). Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 590–597.
8 Johnson, A. E., Pollard, T. J., Berkowitz, S. J., Greenbaum, N. R., Lungren, M. P., Deng, C.-y., Mark, R. G., and Horng, S. (2019). Mimic-cxr, a de-identified publicly available database of chest radiographs with free-text reports. Scientific data, 6(1):317.
9 Larrazabal, A. J., Nieto, N., Peterson, V., Milone, D. H., and Ferrante, E. (2020). Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis. Proceedings of the National Academy of Sciences, 117(23):12592–12594.
10 McMahan, H. B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. In Singh, A. and Zhu, J., editors, Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, volume 54, pages 1273–1282. PMLR.
11 Seyyed-Kalantari, L., Liu, G., McDermott, M. B. A., and Ghassemi, M. (2020). Chexclusion: Fairness gaps in deep chest x-ray classifiers. Pacific Symposium on Biocomputing, 26:232–243
12 Suresh, H. and Guttag, J. (2021). A framework for understanding sources of harm throughout the machine learning life cycle. In Proceedings of the 1st ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, EAAMO ’21, New York, NY, USA. Association for Computing Machinery.
13 Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., and Summers, R. M. (2017). Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2097–2106.
14 Zhang, D. Y., Kou, Z., and Wang, D. (2020). Fairfl: A fair federated learning approach to reducing demographic bias in privacy-sensitive classification models. In 2020 IEEE International Conference on Big Data (Big Data), pages 1051–1060.