1. Agarwal, S. (2020). Trade-offs between fairness, interpretability, and privacy in machine learning. Master’s thesis, University of Waterloo
2. Caton, S., & Haas, C. (2020). Fairness in machine learning: A survey. arXiv preprint arXiv:2010.04053
3. Chouldechova, A. (2017). Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data, 5(2), 153–163.
4. Corbett-Davies, S., & Goel, S. (2018). The measure and mismeasure of fairness: A critical review of fair machine learning. arXiv:1808.00023
5. Crawford, K. (2017). The trouble with bias. in Conference on neural information processing systems (NIPS)