REFERENCES
- Agarwal, A., A. Beygelzimer, M. Dudik, J. Langford, and H. Wallach. 2018. A reductions approach to fair classification. Proceedings of Machine Learning Research 80:60–69.
- Char, D. S., M. D. Abràmoff, and C. Feudtner. 2020. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20 (11):7–17. doi: 10.1080/15265161.2020.1819469.
- Fawcett, T. 2006. An introduction to ROC analysis. Pattern Recognition Letters 27 (8):861–874.
- Hardt, M., E. Price, and N. Srebro. 2016. Equality of opportunity in supervised learning. Advances in Neural Information Processing Systems 29:3315–3323.
- Kleinberg, J., and S. Mullainathan. 2019. Simplicity creates inequity: Implications for fairness, stereotypes, and interpretability. Proceedings of the 2019 ACM Conference on Economics and Computation, 807–808.
- Platt, J. C. 1999. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Advances in large margin classifiers, ed. A. J. Smola, Peter Bartlett, B. SchÎlkopf and D. Schuurmans, 61–74. Cambridge, MA: MIT Press.
- Rajkomar, A., M. Hardt, M. D. Howell, G. Corrado, and M. H. Chin. 2018. Ensuring fairness in machine learning to advance health equity. Annals of Internal Medicine 169 (12):866–872.
- Woodworth, B., S. Gunasekar, M. I. Ohannessian, and N. Srebro. 2017. Learning non-discriminatory predictors. Proceedings of Machine Learning Research 65:1920–1953.
- Zhang, B. H., B. Lemoine, and M. Mitchell. 2018. Mitigating unwanted biases with adversarial learning. Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 335–340. New Orleans, LA, USA.