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English Information

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Authors
# Name
1 Lucas Sena(lucasbeserradesena@gmail.com)
2 Javam Machado(javam.machado@lsbd.ufc.br)

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Reference
# Reference
1 Barocas, S., Hardt, M., and Narayanan, A. (2023). Fairness and machine learning: Limitations and opportunities. MIT press.
2 Caliskan, A., Bryson, J. J., and Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334):183–186.
3 Crenshaw, K. (2013). Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. In Feminist legal theories, pages 23–51. Routledge.
4 Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers), pages 4171–4186.
5 Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., Attariyan, M., and Gelly, S. (2019). Parameter-efficient transfer learning for nlp. In International conference on machine learning, pages 2790–2799. PMLR.
6 Kurita, K., Vyas, N., Pareek, A., Black, A. W., and Tsvetkov, Y. (2019). Measuring bias in contextualized word representations. arXiv preprint arXiv:1906.07337.
7 Lauscher, A., Lueken, T., and Glavaš, G. (2021). Sustainable modular debiasing of language models. arXiv preprint arXiv:2109.03646.
8 Li, Y., Du, M., Song, R., Wang, X., and Wang, Y. (2023). A survey on fairness in large language models. arxiv. doi: 10.48550. arXiv preprint arXiv.2308.10149.
9 May, C., Wang, A., Bordia, S., Bowman, S. R., and Rudinger, R. (2019). On measuring social biases in sentence encoders. arXiv preprint arXiv:1903.10561.
10 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.
11 Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
12 Pennington, J., Socher, R., and Manning, C. D. (2014). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532–1543.
13 Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al. (2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8):9.
14 Sena, L. and Machado, J. (2024). Evaluation of fairness in machine learning models using the uci adult dataset. In Simpósio Brasileiro de Banco de Dados (SBBD), pages 743–749. SBC.
15 Tan, Y. C. and Celis, L. E. (2019). Assessing social and intersectional biases in contextualized word representations. Advances in neural information processing systems, 32.