[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123142-en":3,"doc-seo-123142-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123142,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Connecting algorithmic fairness to quality dimensions in machine learning in official statistics and survey production","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.","AStA Wirtschafts-und Sozialstatistisches Archiv (2024) 18:131–184  \n[https://doi.org/10.1007/s11943-024-00344-2](https://doi.org/10.1007/s11943-024-00344-2)  \nORIGINALVERÖFFENTLICHUNG  \nConnecting algorithmic fairness to quality dimensions in machine learning in official statistics and survey production  \nPatrick Oliver Schenk  · Christoph Kern   \nReceived: 5 May 2023 / Accepted: 23 July 2024 / Published online: 7 October 2024 © The Author(s) 2024  \nAbstract 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.  \nLittle 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.  \n􀀂 Patrick Oliver Schenk · Christoph Kern  \nDepartment of Statistics, LMU Munich, Ludwigstr. 33, 80539 Munich, Germany  \nE-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)  \nChristoph Kern  \nE-Mail: [christoph.kern@stat.uni-muenchen.de](christoph.kern@stat.uni-muenchen.de)  \nChristoph Kern  \nMunich Center for Machine Learning (MCML), Munich, Germany  \nK  \nKeywords Algorithmic Fairness · Quality Dimensions · Machine Learning · Official Statistics · Trustworthy Machine Learning  \nJEL classification C80 · C01 · C52 · C53 · Y80  \n1 Introduction  \nOfficial 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","cbCaikwQkqzMEokD","https://ap.wps.com/l/cbCaikwQkqzMEokD","pdf",1064023,1,54,"English","en",105,"# Introduction\n## Official Statistics, Other Data Producers, and Machine Learning\n# Quality Framework and Fairness Mapping\n## QF4SA quality dimensions and interactions with fairness\n## Fairness as an additional quality dimension\n# Data and Methodology Interactions\n## Data considerations within the framework\n# Contributions to Methodology\n## Official statistics, trustworthy ML, and algorithmic fairness","[{\"question\":\"Why do National Statistical Organizations use machine learning in product development?\",\"answer\":\"They use ML to improve timeliness and cost-effectiveness while aiming to uphold standards for robustness, reproducibility, and accuracy.\"},{\"question\":\"How does the paper connect fairness with the QF4SA quality framework?\",\"answer\":\"It maps QF4SA quality dimensions to algorithmic fairness and analyzes how fairness interacts with each quality dimension.\"},{\"question\":\"What new quality role does the paper propose beyond the existing QF4SA?\",\"answer\":\"It argues for treating fairness as its own additional quality dimension beyond what QF4SA contains so far.\"},{\"question\":\"What is the emphasis regarding data in this framework?\",\"answer\":\"The paper emphasizes data both as a standalone topic and in its interaction with applied methodology, supported by empirical illustrations.\"}]","Connecting algorithmic fairness to quality dimensions in machine learning in official statistics and survey production | 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