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

Aug 4, 2026

National Statistical Organizations increasingly use Machine Learning to improve the timeliness and cost-effectiveness of their products, while maintaining robustness, reproducibility, and accuracy as specified in the Quality Framework for Statistical Algorithms (QF4SA). Research highlights fairness as a prerequisite for safe ML deployment to avoid disparate social impacts, yet fairness has not been explicitly treated as a quality dimension in official-statistics contexts. The study maps QF4SA quality dimensions to algorithmic fairness, extends QF4SA by arguing fairness as an additional quality dimension, and analyzes data and its interactions with applied methodology, supported by empirical illustrations across related domains.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages54

Reading Time2 h 16 min

AccessFree to download

Introduction

Ofﬁcial Statistics, Other Data Producers, and Machine Learning

Quality Framework and Fairness Mapping

QF4SA quality dimensions and interactions with fairness

Fairness as an additional quality dimension

Data and Methodology Interactions

Data considerations within the framework

Contributions to Methodology

Official statistics, trustworthy ML, and algorithmic fairness

AStA Wirtschafts-und Sozialstatistisches Archiv (2024) 18:131–184 [https://doi.org/10.1007/s11943-024-00344-2](https://doi.org/10.1007/s11943-024-00344-2) ORIGINALVERÖFFENTLICHUNG Connecting algorithmic fairness to quality dimensions in machine learning in ofﬁcial statistics and survey production Patrick Oliver Schenk · Christoph Kern Received: 5 May 2023 / Accepted: 23 July 2024 / Published online: 7 October 2024 © The Author(s) 2024 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 asa 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. 􀀂 Patrick Oliver Schenk · Christoph Kern Department of Statistics, LMU Munich, Ludwigstr. 33, 80539 Munich, Germany E-Mail: [patrick.schenk@stat.uni-muenchen.de](patrick.schenk@stat.uni-muenchen.de); [p.o.s.on.stats@gmail.com](p.o.s.on.stats@gmail.com) Christoph Kern E-Mail: [christoph.kern@stat.uni-muenchen.de](christoph.kern@stat.uni-muenchen.de) Christoph Kern Munich Center for Machine Learning (MCML), Munich, Germany K Keywords Algorithmic Fairness · Quality Dimensions · Machine Learning · Ofﬁcial Statistics · Trustworthy Machine Learning JEL classiﬁcation C80 · C01 · C52 · C53 · Y80 1 Introduction Ofﬁcial Statistics, Other Data Producers, and Machine Learning Machine Learning (ML, see Table 1 for a list of abbreviations) is now widely used in government, state, federal, and similar agencies (Engstrom et al. 2020; IPS Observatory 2024; TAG Register 2024; AlgorithmWatch 2019; Domscheit-Berg 2024) . Ofﬁcial Statistics, e.g., in International, State, and National Statistical Organizations (NSOs for short), is one such area (see Beck et al. 2018a, Chap. 2 and Sect. 2) . The introduction of ML can be seen as part of the modernization efforts at NSOs: these happen on the (cross-)organizational level (e.g., the UNECE High-Level Group for the Modernisation of Ofﬁcial Statistics, see [https://statswiki.unece.org/display/](https://statswiki.unece.org/display/)[ ](https://statswiki.unece.org/display/)hlgbas) but also within organizations because of their mandates for ongoing revision of methods, data sources, and products and, more indirectly, because of their operating principles of e.g., timeliness, and cost-effectiveness (Eurostat 2017) . In addition, there is increased competition from other producers of data and of statistics who offer products that are, e.g., new or more timely, often made possible by gained innovation advantages or because they are less bound

FAQs

Why do National Statistical Organizations use machine learning in product development?

They use ML to improve timeliness and cost-effectiveness while aiming to uphold standards for robustness, reproducibility, and accuracy.

How does the paper connect fairness with the QF4SA quality framework?

It maps QF4SA quality dimensions to algorithmic fairness and analyzes how fairness interacts with each quality dimension.

What new quality role does the paper propose beyond the existing QF4SA?

It argues for treating fairness as its own additional quality dimension beyond what QF4SA contains so far.

What is the emphasis regarding data in this framework?

The paper emphasizes data both as a standalone topic and in its interaction with applied methodology, supported by empirical illustrations.

Aug 4, 2026

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