[15] Creel, K. and Hellman, D. (2022). The algorithmic leviathan: arbitrariness, fairness, and opportunity in algorithmic decision-making systems. Canadian Journal of Philosophy, 52(1):26-43.
[16] Cury, C. R. J. (2022). Instituto Nacional de estudos e pesquisas educaciones anisio teixeira: uma trajetória em busca de umaeducatedao de qualidade.
[17] D'Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., Chen, C., Deaton, J., Eisenstein, J., Hoffman, M. D., et al. (2022). Underspecification presents challenges for credibility in modern machine learning. The Journal of Machine Learning Research, 23(1):10237-10297.
[18] Dwork, C., Feldman, V., Hardt, M., Pitassi, T., Reingold, O., and Roth, A. L. (2015). Preserving statistical validity in adaptive data analysis. In Proceedings of the forty-seventh annual ACM symposium on Theory of computing, pages 117-126.
[19] Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R. (2012). Fairness through awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, ITCS '12, page 214-226, New York, NY, USA. Association for Computing Machinery.
[20] Fisher, A., Rudin, C., and Dominici, F. (2019). All models are wrong, but many are useful: Learning a variable's importance by studying an entire class of prediction models simultaneously. J. Mach. Learn. Res., 20(177):1-81.
[21] Ganesh, P., Chang, H., Strobel, M., and Shokri, R. (2023). On the impact of machine learning randomness on group fairness. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT '23, pages 1789-1800, New York, NY, USA. Association for Computing Machinery.
[22] Hajek, B. and Raginsky, M. (2019). Statistical learning theory. Lecture Notes, 387.
[23] Hardt, M., Price, E., and Srebro, N. (2016). Equality of opportunity in supervised learning. Advances in neural information processing systems, 29.
[24] Hébert-Johnson, U., Kim, M., Reingold, O., and Rothblum, G. (2018). Multicalibration: Calibration for the (computationally-identifiable) masses. In International Conference on Machine Learning, pages 1939–1948. PMLR.
[25] Hort, M., Chen, Z., Zhang, J. M., Sarro, F., and Harman, M. (2022). Bia mitigation for machine learning classifiers: A comprehensive survey. arXiv preprint arXiv:2207.07068.
[26] Hsu, H. and Calmon, F. d. P. (2022). Rashomon capacity: A metric for predictive multiplicity in probabilistic classification. arXiv preprint arXiv:2206.01295.
[27] Ingels, S. J., Pratt, D. J., Herget, D. R., Burns, L. J., Dever, J. A., Ottem, R., Rogers, J. E., Jin, Y., and Leinwand, S. (2011). High school longitudinal study of 2009 (hsls: 09): Base-year data file documentation. nces 2011-328. National Center for Education Statistics.
[28] Jeong, H., Wang, H., and Calmon, F. P. (2022). Fairness without imputation: A decision tree approach for fair prediction with missing values. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 9558-9566.
[29] Kamiran, F., Karim, A., and Zhang, X. (2012). Decision theory for discrimination-aware classification. In 2012 IEEE 12th international conference on data mining, pages 924-929. IEEE.
[30] Kim, M. P., Ghorbani, A., and Zou, J. (2019). Multiaccuracy: Black-box post-processing for fairness in classification. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, pages 247-254.
[31] Krco, N., Laugel, T., Loubes, J.-M., and Detyniecki, M. (2023). When mitigating bias is unfair: A comprehensive study on the impact of bias mitigation algorithms. arXiv preprint arXiv:2302.07185.