  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.
