SlowGuess/NeurIPS_2023_OCR huggingface dataset:
[1] M. Du, F. Yang, N. Zou, and X. Hu, “Fairness in deep learning: A computational perspective,” IEEE Intelligent Systems, vol. 36, no. 4, pp. 25–34, 2020.
[2] N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan, "A survey on bias and fairness in machine learning," ACM Computing Surveys (CSUR), vol. 54, no. 6, pp. 1-35, 2021.
[3] B. Kim, H. Kim, K. Kim, S. Kim, and J. Kim, "Learning not to learn: Training deep neural networks with biased data," in IEEE Conference on Computer Vision and Pattern Recognition, 2019.
[4] R. Geirhos, J.-H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F. A. Wichmann, "Shortcut learning in deep neural networks," Nature Machine Intelligence, vol. 2, no. 11, pp. 665-673, 2020.
[5] S. Sagawa, A. Raghunathan, P. W. Koh, and P. Liang, “An investigation of why overparameterization exacerbates spurious correlations,” in International Conference on Machine Learning, pp. 8346–8356, PMLR, 2020.
[6] R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel, "Imagenetrained cnns are biased towards texture; increasing shape bias improves accuracy and robustness," arXiv preprint arXiv:1811.12231, 2018.
[7] S. Sagawa, P. W. Koh, T. B. Hashimoto, and P. Liang, “Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization,” in International Conference on Learning Representations, 2020.
[8] E. Tartaglione, C. A. Barbano, and M. Grangetto, "End: Entangling and disentangling deep representations for bias correction," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13508-13517, June 2021.
[9] S. Sagawa, A. Raghunathan, P. W. Koh, and P. Liang, "An investigation of why overparameterization exacerbates spurious correlations," in International Conference on Machine Learning, pp. 8346-8356, PMLR, 2020.
[10] T. Brennan, W. Dieterich, and B. Ehret, "Evaluating the predictive validity of the compas risk and needs assessment system," Criminal Justice and behavior, vol. 36, no. 1, pp. 21-40, 2009.
[11] J. F. Mahoney and J. M. Mohen, "Method and system for loan origination and underwriting," Oct. 23 2007. US Patent 7,287,008.
[12] M. Bogen and A. Rieke, "Help wanted: An examination of hiring algorithms, equity, and bias," 2018.
[13] D. Pessach and E. Shmueli, “A review on fairness in machine learning,” ACM Computing Surveys (CSUR), vol. 55, no. 3, pp. 1–44, 2022.
[14] O. Parraga, M. D. More, C. M. Oliveira, N. S. Gavenski, L. S. Kupssinski, A. Medronha, L. V. Moura, G. S. Simões, and R. C. Barros, “Debiasing methods for fairer neural models in vision and language research: A survey,” arXiv preprint arXiv:2211.05617, 2022.
[15] Y. Li and N. Vasconcelos, "Repair: Removing representation bias by dataset resampling," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 9572-9581, 2019.