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 prevent disparate social impacts in practice. However, fairness has not yet been explicitly discussed as a quality aspect in the context of the application of ML at NSOs. We employ Yung et al. (2022)'s QF4SA quality framework and present a mapping of its quality dimensions to algorithmic fairness. We thereby extend the QF4SA framework in several ways: we argue for fairness as its own quality dimension, we investigate the interaction of fairness with other dimensions, and we explicitly address data, both on its own and its interaction with applied methodology. In parallel with empirical illustrations, we show how our mapping can contribute to methodology in the domains of official statistics, algorithmic fairness, and trustworthy machine learning.

翻译：国家统计机构（NSOs）日益依赖机器学习（ML）以提升其产品的时效性和成本效益。在引入ML解决方案时，NSOs必须确保在鲁棒性、可重复性和准确性方面维持高标准，正如《统计算法质量框架》（QF4SA；Yung等人，2022）所规定的那样。与此同时，越来越多的研究关注公平性，将其视为安全部署ML以防止实践中产生不同社会影响的前提条件。然而，在NSOs应用ML的背景下，公平性尚未被明确作为质量方面进行讨论。我们采用Yung等人（2022）的QF4SA质量框架，并展示了其质量维度与算法公平性的映射关系。由此，我们以多种方式扩展了QF4SA框架：主张将公平性作为独立的质量维度，研究公平性与其他维度的相互作用，并明确解决数据本身及其与应用方法交互的问题。结合实证示例，我们展示了这一映射如何为官方统计、算法公平性及可信机器学习领域的方法论做出贡献。