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Dabei folgt sie den europ\u00e4ischen Qualit\u00e4tsrahmenwerken, die auf nationaler Ebene in Form von Qualit\u00e4tshandb\u00fcchern konkretisiert und operationalisiert werden, sich jedoch bis dato hinsichtlich Ausgestaltung und Interpretation an den Anforderungen der \u201eklassischen\u201c Statistikproduktion orientieren. Der zunehmende Einsatz maschineller Lernverfahren (ML) in der amtlichen Statistik muss daher zur Erf\u00fcllung des Qualit\u00e4tsanspruchs durch ein spezifisches, darauf zugeschnittenes Qualit\u00e4tsrahmenwerk begleitet werden. Das vorliegende Papier leistet einen Beitrag zur Erarbeitung eines solchen Qualit\u00e4tsrahmenwerks f\u00fcr den Einsatz von ML in der amtlichen Statistik, indem es (1)\u00a0durch den Vergleich mit bestehenden Qualit\u00e4tsgrunds\u00e4tzen des Verhaltenskodex f\u00fcr Europ\u00e4ische Statistiken relevante Qualit\u00e4tsdimensionen f\u00fcr ML identifiziert und (2)\u00a0diese unter Ber\u00fccksichtigung der besonderen methodischen Gegebenheiten von ML ausarbeitet. Dabei (2a)\u00a0erg\u00e4nzt es bestehende Vorschl\u00e4ge durch den Aspekt der Robustheit, (2b)\u00a0stellt Bezug zu den Querschnittsthemen Machine Learning Operations (MLOps) und Fairness her und (2c)\u00a0schl\u00e4gt vor, wie die Qualit\u00e4tssicherung der einzelnen Dimensionen in der Praxis der amtlichen Statistik ausgestaltet werden kann. Diese Arbeit liefert die konzeptionelle Grundlage, um Qualit\u00e4tsindikatoren f\u00fcr ML-Verfahren formell in die Instrumente des Qualit\u00e4tsmanagements im Statistischen Verbund zu \u00fcberf\u00fchren und damit langfristig den hohen Qualit\u00e4tsstandard amtlicher Statistik auch bei Nutzung neuer Verfahren zu sichern.<\/jats:p>","DOI":"10.1007\/s11943-023-00329-7","type":"journal-article","created":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T14:02:27Z","timestamp":1700229747000},"page":"253-303","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Qualit\u00e4tsdimensionen maschinellen Lernens in der amtlichen Statistik","Quality Dimensions of Machine Learning in Official Statistics"],"prefix":"10.1007","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-9852-8226","authenticated-orcid":false,"given":"Younes","family":"Saidani","sequence":"first","affiliation":[]},{"given":"Florian","family":"Dumpert","sequence":"additional","affiliation":[]},{"given":"Christian","family":"Borgs","sequence":"additional","affiliation":[]},{"given":"Alexander","family":"Brand","sequence":"additional","affiliation":[]},{"given":"Andreas","family":"Nickl","sequence":"additional","affiliation":[]},{"given":"Alexandra","family":"Rittmann","sequence":"additional","affiliation":[]},{"given":"Johannes","family":"Rohde","sequence":"additional","affiliation":[]},{"given":"Christian","family":"Salwiczek","sequence":"additional","affiliation":[]},{"given":"Nina","family":"Storfinger","sequence":"additional","affiliation":[]},{"given":"Selina","family":"Straub","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,17]]},"reference":[{"key":"329_CR1","unstructured":"Ahlborn M, Draken F, Schulz V (2021) Qualit\u00e4tssicherung in der amtlichen Statistik: Large Cases Unit. 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