[92] A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, et al., "Language models are unsupervised multitask learners," OpenAI blog, vol. 1, no. 8, p. 9, 2019.
[93] S. Seo, J.-Y. Lee, and B. Han, "Unsupervised learning of unbiased representations with pseudo-attributes," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16742–16751, 2022.
[94] R. Zmigrod, S. J. Mielke, H. Wallach, and R. Cotterell, “Counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology,” arXiv preprint arXiv:1906.04571, 2019.
[95] K. Webster, X. Wang, I. Tenney, A. Beutel, E. Pittler, E. Pavlick, J. Chen, E. Chi, and S. Petrov, "Measuring and reducing gendered correlations in pre-trained models," arXiv preprint arXiv:2010.06032, 2020.
[96] Y. Goyal, Z. Wu, J. Ernst, D. Batra, D. Parikh, and S. Lee, "Counterfactual visual explanations," in International Conference on Machine Learning, pp. 2376-2384, PMLR, 2019.
[97] S. Dash, V. N. Balasubramanian, and A. Sharma, “Evaluating and mitigating bias in image classifiers: A causal perspective using counterfactuals,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 915–924, 2022.
[98] H. Kim, S. Shin, J. Jang, K. Song, W. Joo, W. Kang, and I.-C. Moon, "Counterfactual fairness with disentangled causal effect variational autoencoder," in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 8128-8136, 2021.
[99] J. Cheong, S. Kalkan, and H. Gunes, "Counterfactual fairness for facial expression recognition," in European Conference on Computer Vision, pp. 245-261, Springer, 2022.
[100] S. Basu, P. Pope, and S. Feizi, "Influence functions in deep learning are fragile," arXiv preprint arXiv:2006.14651, 2020.
[101] J. Bae, N. Ng, A. Lo, M. Ghassemi, and R. B. Grosse, "If influence functions are the answer, then what is the question?", Advances in Neural Information Processing Systems, vol. 35, pp. 17953-17967, 2022.
[102] G. Pruthi, F. Liu, S. Kale, and M. Sundararajan, "Estimating training data influence by tracing gradient descent," Advances in Neural Information Processing Systems, vol. 33, pp. 19920-19930, 2020.
[103] C.-K. Yeh, J. Kim, I. E.-H. Yen, and P. K. Ravikumar, “Representer point selection for explaining deep neural networks,” Advances in neural information processing systems, vol. 31, 2018.
[104] R. D. Cook and S. Weisberg, “Characterizations of an empirical influence function for detecting influential cases in regression,” Technometrics, vol. 22, no. 4, pp. 495–508, 1980.
[105] R. D. Cook and S. Weisberg, *Residuals and influence in regression*. New York: Chapman and Hall, 1982.
[106] B. A. Pearlmutter, "Fast exact multiplication by the hessian," Neural computation, vol. 6, no. 1, pp. 147-160, 1994.
[107] N. Agarwal, B. Bullins, and E. Hazan, "Second-order stochastic optimization for machine learning in linear time," The Journal of Machine Learning Research, vol. 18, no. 1, pp. 4148-4187, 2017.
[108] J. Pearl et al., "Models, reasoning and inference," Cambridge, UK: Cambridge University Press, vol. 19, no. 2, 2000.
[109] S. R. Pfohl, T. Duan, D. Y. Ding, and N. H. Shah, "Counterfactual reasoning for fair clinical risk prediction," in Machine Learning for Healthcare Conference, pp. 325-358, PMLR, 2019.