[GM14] Sonal Gupta and Christopher D Manning. Improved pattern learning for bootstrapped entity extraction. In Proceedings of the Eighteenth Conference on Computational Natural Language Learning, pages 98–108, 2014.
[GNRV19] Vivek Gupta, Pegah Nokhiz, Chitradeep Dutta Roy, and Suresh Venkatasubramanian. Equalizing recourse across groups. arXiv preprint arXiv:1909.03166, 2019.
$\left[\mathrm{GZS}^{+}22\right]$  Ozgur Guldogan, Yuchen Zeng, Jy-yong Sohn, Ramtin Pedarsani, and Kangwook Lee. Equal improvability: A new fairness notion considering the long-term impact. arXiv preprint arXiv:2210.06732, 2022.
[HNG19] Hoda Heidari, Vedant Nanda, and Krishna P Gummadi. On the long-term impact of algorithmic decision policies: Effort unfairness and feature segregation through social learning. arXiv preprint arXiv:1903.01209, 2019.
[HP516] Moritz Hardt, Eric Price, and Nati Srebro. Equality of opportunity in supervised learning. Advances in neural information processing systems, 29, 2016.
[HU20] Laura Hanu and Unitary team. Detoxify. Github. https://github.com/unitaryai/detoxify, 2020.
[HWZW20] Wen Huan, Yongkai Wu, Lu Zhang, and Xintao Wu. Fairness through equality of effort. In Companion Proceedings of the Web Conference 2020, pages 743-751, 2020.
$\left[\mathrm{K}^{+}96\right]$  Ron Kohavi et al. Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid. In Kdd, volume 96, pages 202-207, 1996.
[KGZ19] Michael P Kim, Amirata Ghorbani, and James Zou. 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, 2019.
[KL22] Nikola Konstantinov and Christoph H Lampert. Fairness-aware pac learning from corrupted data. The Journal of Machine Learning Research, 23(1):7173-7232, 2022.
[KLRS17] Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva. Counterfactual fairness. Advances in neural information processing systems, 30, 2017.
[KOS11] David R Karger, Sewoong Oh, and Devavrat Shah. Iterative learning for reliable crowdsourcing systems. In Advances in neural information processing systems, pages 1953-1961, 2011.
[KS84] Martin Knott and Cyril S Smith. On the optimal mapping of distributions. Journal of Optimization Theory and Applications, 43:39-49, 1984.
[KSM+21] Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al. Wilds: A benchmark of in-the-wild distribution shifts. In International Conference on Machine Learning, pages 5637-5664. PMLR, 2021.
[LLWT15] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Deep learning face attributes in the wild. In Proceedings of International Conference on Computer Vision (ICCV), December 2015.
[LVS] Hunter Lang, Aravindan Vijayaraghavan, and David Sontag. Training subset selection for weak supervision. In Advances in Neural Information Processing Systems.
[MBSJ09] Mike Mintz, Steven Bills, Rion Snow, and Dan Jurafsky. Distant supervision for relation extraction without labeled data. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2-Volume 2, pages 1003–1011. Association for Computational Linguistics, 2009.