1. Alekh Agarwal , Alina Beygelzimer , Miroslav Dudík , John Langford , and Hanna Wallach . 2018 . A reductions approach to fair classification . In International Conference on Machine Learning. PMLR, 60–69 . Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach. 2018. A reductions approach to fair classification. In International Conference on Machine Learning. PMLR, 60–69.
2. Julia Angwin , Jeff Larson , Surya Mattu , and Lauren Kirchner . 2016. Machine bias: There’s software used across the country to predict future criminals, and it’s biased against blacks. ProPublica ( 2016 ). Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine bias: There’s software used across the country to predict future criminals, and it’s biased against blacks. ProPublica (2016).
3. Samuel A Assefa , Danial Dervovic , Mahmoud Mahfouz , Robert E Tillman , Prashant Reddy , and Manuela Veloso . 2020 . Generating synthetic data in finance: opportunities, challenges and pitfalls . In Proceedings of the First ACM International Conference on AI in Finance. 1–8. Samuel A Assefa, Danial Dervovic, Mahmoud Mahfouz, Robert E Tillman, Prashant Reddy, and Manuela Veloso. 2020. Generating synthetic data in finance: opportunities, challenges and pitfalls. In Proceedings of the First ACM International Conference on AI in Finance. 1–8.
4. Solon Barocas Moritz Hardt and Arvind Narayanan. 2019. Fairness and Machine Learning. fairmlbook.org. http://www.fairmlbook.org. Solon Barocas Moritz Hardt and Arvind Narayanan. 2019. Fairness and Machine Learning. fairmlbook.org. http://www.fairmlbook.org.
5. Big data’s disparate impact;Barocas Solon;Calif. L. Rev.,2016