Justiça em Modelos de Apoio a Processos de Tomada de Decisão com Uso de Aprendizado Federado
Resumo
Desbalanceamentos nos dados de treinamento podem resultar em modelos de aprendizado de máquina enviesados. Em processos de tomada de decisão na área médica, vieses ligados aos atributos demográficos para diferentes populações podem levar a tratamentos discriminatórios. Uma solução possível é obter dados mais diversos a partir da colaboração entre hospitais, mas restrições de privacidade impõem desafios. Nesse contexto, o aprendizado federado permite desenvolver modelos empregando dados de múltiplas instituições, respeitando essas restrições. Este trabalho propõe o uso do aprendizado federado para desenvolver modelos com desempenho equitativo entre diferentes populações, explorando o potencial dessa abordagem para promover a justiça na área médica.
Palavras-chave:
aprendizado federado, justiça, viés demográfico, imagens médicas
Referências
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.
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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.
Deng, L. (2012). The mnist database of handwritten digit images for machine learning research. IEEE Signal Processing Magazine, 29(6):141–142.
Ezzeldin, Y. H. (2023). Fairfed: Enabling group fairness in federated learning. In Proceedings of the AAAI Conference on Artificial Intelligence.
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.
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.
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.
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.
Seyyed-Kalantari, L., Liu, G., McDermott, M. B. A., and Ghassemi, M. (2020). Chex-clusion: Fairness gaps in deep chest x-ray classifiers. Pacific Symposium on Biocomputing, 26:232–243.
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.
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.
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.
Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D. (2020). Invariant risk minimization. Preprint disponível em: [link].
Brasil (2018). Lei nº 13.709, de 14 de agosto de 2018. Diário Oficial da União.
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.
Deng, L. (2012). The mnist database of handwritten digit images for machine learning research. IEEE Signal Processing Magazine, 29(6):141–142.
Ezzeldin, Y. H. (2023). Fairfed: Enabling group fairness in federated learning. In Proceedings of the AAAI Conference on Artificial Intelligence.
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.
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.
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.
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.
Seyyed-Kalantari, L., Liu, G., McDermott, M. B. A., and Ghassemi, M. (2020). Chex-clusion: Fairness gaps in deep chest x-ray classifiers. Pacific Symposium on Biocomputing, 26:232–243.
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.
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.
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.
Publicado
14/10/2024
Como Citar
DO CARMO, Êrica Peters; TRAINA, Agma J. M.; TRAINA JR., Caetano.
Justiça em Modelos de Apoio a Processos de Tomada de Decisão com Uso de Aprendizado Federado. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 39. , 2024, Florianópolis/SC.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 883-889.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2024.243139.
