Egalitarianism and Algorithmic Fairness

Author:

Holm SuneORCID

Funder

Danmarks Frie Forskningsfond

Publisher

Springer Science and Business Media LLC

Subject

History and Philosophy of Science,Philosophy

Reference31 articles.

1. Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks. ProPublica https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

2. Baumann, J., Hertweck, C., Loi, M., & Heitz, C. (2022). Distributive justice as the foundational premise of fair ML: Unification, extension, and interpretation of group fairness metrics. https://arxiv.org/abs/2206.02897.

3. Berk, R., Heidari, H., Jabbari, S., Kearns, M., & Roth, A. (2021). Fairness in criminal justice risk assessments: The state of the art. Sociological Methods & Research, 50(1), 3–44. https://doi.org/10.1177/0049124118782533

4. Binns, R. (2017). Fairness in machine learning: Lessons from political philosophy. Proceedings of Machine Learning Research, 81, 1–11.

5. Broome, J. (1990). Fairness. Proceedings of the Aristotelian Society, 91, 87–101.

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