Web: http://arxiv.org/abs/2205.05512

May 12, 2022, 1:11 a.m. | Michele Loi, Christoph Heitz

cs.LG updates on arXiv.org arxiv.org

In this paper, we provide a moral analysis of two criteria of statistical
fairness debated in the machine learning literature: 1) calibration between
groups and 2) equality of false positive and false negative rates between
groups. In our paper, we focus on moral arguments in support of either measure.
The conflict between group calibration vs. false positive and false negative
rate equality is one of the core issues in the debate about group fairness
definitions among practitioners. For any thorough …

arxiv decision fairness philosophy theory

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