Feb. 15, 2024, 5:43 a.m. | Patrick Oliver Schenk, Christoph Kern

cs.LG updates on arXiv.org arxiv.org

arXiv:2402.09328v1 Announce Type: cross
Abstract: National Statistical Organizations (NSOs) increasingly draw on Machine Learning (ML) to improve the timeliness and cost-effectiveness of their products. When introducing ML solutions, NSOs must ensure that high standards with respect to robustness, reproducibility, and accuracy are upheld as codified, e.g., in the Quality Framework for Statistical Algorithms (QF4SA; Yung et al. 2022). At the same time, a growing body of research focuses on fairness as a pre-condition of a safe deployment of ML to …

abstract accuracy algorithmic fairness arxiv cost cs.lg dimensions fairness machine machine learning organizations production products quality reproducibility robustness solutions standards statistical statistics stat.me stat.ml survey type

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