1. Agarwal, A., Dudík, M., Wu, Z.S.: Fair regression: quantitative definitions and reduction-based algorithms. In: International Conference on Machine Learning (2019)
2. Angwin, J., Larson, J., Mattu, S., Kirchner, L.: Machine bias: there’s software used across the country to predict future criminals. and it’s biased against blacks (2016). https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
3. del Barrio, E., Gordaliza, P., Loubes, J.M.: Review of mathematical frameworks for fairness in machine learning. arXiv preprint arXiv:2005.13755 (2020)
4. Bebee, B.: Blazegraph wiki. https://github.com/blazegraph/database/wiki
5. Bellamy, R.K.E., et al.: AI fairness 360: an extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias (2018)