[17] Marc N Elliott, Peter A Morrison, Allen Fremont, Daniel F McCaffrey, Philip Pantoja, and Nicole Lurie. Using the census bureau's surname list to improve estimates of race/ethnicity and associated disparities. Health Services and Outcomes Research Methodology, 9:69-83, 2009.
[18] Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkata-subramanian. Certifying and removing disparate impact. In proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pages 259-268, 2015.
[19] Judy Wawira Gichoya, Imon Banerjee, Ananth Reddy Bhimireddy, John L Burns, Leo Anthony Celi, Li-Ching Chen, Ramon Correa, Natalie Dullerud, Marzyeh Ghassemi, Shih-Cheng Huang, et al. Ai recognition of patient race in medical imaging: a modelling study. The Lancet Digital Health, 4(6):e406-e414, 2022.
[20] Maya Gupta, Andrew Cotter, Mahdi Milani Fard, and Serena Wang. Proxy fairness, 2018.
[21] Moritz Hardt, Eric Price, and Nati Srebro. Equality of opportunity in supervised learning. Advances in neural information processing systems, 29, 2016.
[22] Hoda Heidari, Michele Loi, Krishna P Gummadi, and Andreas Krause. A moral framework for understanding fair ml through economic models of equality of opportunity. In Proceedings of the conference on fairness, accountability, and transparency, pages 181-190, 2019.
[23] Kosuke Imai and Kabir Khanna. Improving ecological inference by predicting individual ethnicity from voter registration records. Political Analysis, 24(2):263-272, 2016.
[24] Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghloo, Robyn Ball, Katie Shpanskaya, et al. Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 590-597, 2019.
[25] Nathan Kallus, Xiaojie Mao, and Angela Zhou. Assessing algorithmic fairness with unobserved protected class using data combination. Management Science, 68(3):1959-1981, 2022.
[26] Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of naacL-HLT, volume 1, page 2, 2019.
[27] Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In International Conference on Learning Representations (ICLR), San Diego, CA, USA, 2015.
[28] Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva. Counterfactual fairness. Advances in neural information processing systems, 30, 2017.
[29] Stefano Leone. Fifa 21 complete player dataset, Oct 2020. URL https://www.kaggle.com/datasets/stefanoleone992/fifa-21-complete-player-dataset.
[30] Andrew Mason. Levelling the playing field: The idea of equal opportunity and its place in egalitarian thought. OUP Oxford, 2006.
[31] Cecilia Munoz, DJ Patil, and Megan Smith. Big data: A report on algorithmic systems, opportunity, and civil rights. Executive Office of the President, 2016.
[32] Razieh Nabi and Ilya Shpitser. Fair inference on outcomes. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 32, 2018.
[33] Radiological Society of North America. Rsna pneumonia detection challenge, kaggle competition, 2018.
[34] Flavien Prost, Pranjal Awasthi, Nick Blumm, Aditee Kumthekar, Trevor Potter, Li Wei, Xuezhi Wang, Ed H Chi, Jilin Chen, and Alex Beutel. Measuring model fairness under noisy covariates: A theoretical perspective. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, pages 873-883, 2021.