Reference

[DPM+22]

Hyungrok Do, Preston Putzel, Axel S Martin, Padhraic Smyth, and Judy Zhong. Fair generalized linear models with a convex penalty. In International Conference on Machine Learning, 5286–5308. PMLR, 2022.

[DOBD+18]

Michele Donini, Luca Oneto, Shai Ben-David, John S Shawe-Taylor, and Massimiliano Pontil. Empirical risk minimization under fairness constraints. Advances in neural information processing systems, 2018.

[FFM+15]

Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian. Certifying and removing disparate impact. In proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, 259–268. 2015.

[HPS16]

Moritz Hardt, Eric Price, and Nati Srebro. Equality of opportunity in supervised learning. Advances in neural information processing systems, 2016.

[JN20]

Heinrich Jiang and Ofir Nachum. Identifying and correcting label bias in machine learning. In International Conference on Artificial Intelligence and Statistics, 702–712. PMLR, 2020.

[KC12]

Faisal Kamiran and Toon Calders. Data preprocessing techniques for classification without discrimination. Knowledge and information systems, 33(1):1–33, 2012.

[LGW19]

Preethi Lahoti, Krishna P Gummadi, and Gerhard Weikum. Ifair: learning individually fair data representations for algorithmic decision making. In 2019 ieee 35th international conference on data engineering, 1334–1345. IEEE, 2019.

[LL22]

Peizhao Li and Hongfu Liu. Achieving fairness at no utility cost via data reweighing with influence. In International Conference on Machine Learning, 12917–12930. PMLR, 2022.

[SJ19]

Ashudeep Singh and Thorsten Joachims. Policy learning for fairness in ranking. Advances in neural information processing systems, 2019.

[ZVRG17]

Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rogriguez, and Krishna P Gummadi. Fairness constraints: mechanisms for fair classification. In Artificial intelligence and statistics, 962–970. PMLR, 2017.

[ZBC+17]

Meike Zehlike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, and Ricardo Baeza-Yates. Fa* ir: a fair top-k ranking algorithm. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 1569–1578. 2017.