The problem of fairness in tools for algorithmic fairness

Author:

Sun Xiao-yu,Ye BinORCID,Xia Bao-hua

Funder

National Social Science Fund of China

Publisher

Springer Science and Business Media LLC

Reference74 articles.

1. Agarwal, A., Beygelzimer, A., Dudík, M., Langford, J., & Wallach, H. (2018). A reductions approach to fair classification. In international conference on machine learning, 60–69. PMLR. https://doi.org/10.48550/arXiv.1803.02453. Accessed 6 June 2023

2. Aïvodji, U., Arai, H., Fortineau, O., et al. (2019). Fairwashing: the risk of rationalization. In international conference on machine learning. PMLR. pp. 161–170.

3. Akpinar, N. J., Nagireddy, M., Stapleton, L., et al. (2022). A sandbox tool to bias (stress)-test fairness algorithms. arxiv preprint arxiv:2204.10233.

4. Aziz, H., Li, B., & Wu, X. (2020). Approximate and strategyproof maximin share allocation of chores with ordinal preferences (arXiv:2012.13884). http://arxiv.org/abs/2012.13884. Accessed 6 June 2023

5. Baker, R.S., Hawn, A.: Algorithmic bias in education. Int. J. Artif. Intell. Educ. 32(4), 1052–1092 (2022)

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