Connecting algorithmic fairness to quality dimensions in machine learning in official statistics and survey production

Patrick Oliver Schenk () and Christoph Kern ()
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Patrick Oliver Schenk: LMU Munich
Christoph Kern: LMU Munich

AStA Wirtschafts- und Sozialstatistisches Archiv, 2024, vol. 18, issue 2, No 2, 184 pages

Abstract: 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, Statistical Journal of the IAOS). 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 the QF4SA quality framework and present a mapping of its quality dimensions to algorithmic fairness. We thereby extend the QF4SA framework in several ways: First, we investigate the interaction of fairness with each of these quality dimensions. Second, we argue for fairness as its own, additional quality dimension, beyond what is contained in the QF4SA so far. Third, we emphasize and 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. Little to no prior knowledge of ML, fairness, and quality dimensions in official statistics is required as we provide introductions to these subjects. These introductions are also targeted to the discussion of quality dimensions and fairness.

Keywords: Algorithmic Fairness; Quality Dimensions; Machine Learning; Official Statistics; Trustworthy Machine Learning (search for similar items in EconPapers)
JEL-codes: C01 C52 C53 C80 Y80 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11943-024-00344-2

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