[1] Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, and Aaron Roth. Fairness in criminal justice risk assessments: The state of the art. Sociological Methods & Research, 50(1):3-44, 2021.
[2] Jeff Larson, Surya Mattu, Lauren Kirchner and Julia Angwin. How we analyzed the compas recidivism algorithm, 2016. URL: https://www_propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm.
[3] Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. Inherent trade-offs in the fair determination of risk scores. In Conference on Innovations in Theoretical Computer Science (ITCS), 2017.
[4] Alexandra Chouldechova. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. *Big Data*, 5(2):153–163, 2017. doi:10.1089/big.2016.0047.
[5] Solon Barocas and Andrew D. Selbst. Big data's disparate impact. California Law Review, (104):671-732, 2016. doi:10.15779/Z38BG31.
[6] Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR), 54(6):1-35, 2021.
[7] J. Larson, S. Mattu, L. Kirchner, and J. Angwin. COMPAS Broward county dataset. 2016. URL: https://github.com/propublica/compas-analysis.
[8] Nil-Jana Akpinar, Manish Nagireddy, Logan Stapleton, Hao-Fei Cheng, Haiyi Zhu, Steven Wu, and Hoda Heidari. A sandbox tool to bias (stress)-test fairness algorithms. arXiv preprint arXiv:2204.10233, 2022.
[9] Rodrigo L. Cardoso, Wagner Meira Jr, Virgilio Almeida, and Mohammed J. Zaki. A framework for benchmarking discrimination-aware models in machine learning. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, pages 437-444, 2019.
[10] Mohit Sharma, Amit Deshpande, and Rajiv Ratn Shah. On testing and comparing fair classifiers under data bias. arXiv preprint arXiv:2302.05906, 2023.
[11] Michelle Bao, Angela Zhou, Samantha Zottola, Brian Brubach, Sarah Desmarais, Aaron Horowitz, Kristian Lum, and Suresh Venkatasubramanian. It's compulsicated: The messy relationship between rai datasets and algorithmic fairness benchmarks. 35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks., 2021.
[12] Alexander D'Amour, Hansa Srinivasan, James Atwood, Pallavi Baljekar, D Sculley, and Yoni Halpern. Fairness is not static: deeper understanding of long term fairness via simulation studies. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pages 525-534, 2020.
[13] Nianyun Li, Naman Goel, and Elliott Ash. Data-centric factors in algorithmic fairness. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, pages 396-410, 2022.
[14] Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé Iii, and Kate Crawford. Datasheets for datasets. Communications of the ACM, 64(12):86-92, 2021.
[15] A Feder Cooper, Solon Barocas, Christopher De Sa, and Siddhartha Sen. Variance, self-consistency, and arbitrariness in fair classification. arXiv preprint arXiv:2301.11562, 2023.
[16] Miri Zilka, Bradley Butcher, and Adrian Weller. A survey and datasheet repository of publicly available us criminal justice datasets. 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks., 35:28008-28022, 2022.