May 16, 2024, 4:41 a.m. | Yijun Bian, Yujie Luo

cs.LG updates on

arXiv:2405.09251v1 Announce Type: new
Abstract: Providing various machine learning (ML) applications in the real world, concerns about discrimination hidden in ML models are growing, particularly in high-stakes domains. Existing techniques for assessing the discrimination level of ML models include commonly used group and individual fairness measures. However, these two types of fairness measures are usually hard to be compatible with each other, and even two different group fairness measures might be incompatible as well. To address this issue, we investigate …

abstract applications arxiv bias concerns cs.lg discrimination domains extra fairness hidden however machine machine learning ml models type world

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