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  • 1. On the Interplay of Legal Requirements, Quality Aspects and Ethical Risks when using Machine Learning in German Official Statistics Archived
    Articles and reports: 11-522-X202500100014
    Description: Artificial intelligence (AI) with its subfield machine learning (ML) has found its way into administration in general and also into official statistics in Germany in particular. This paper highlights the ethical issues that may arise when using AI/ML in official statistics and examines whether a separate ethical framework is needed to deal with these issues appropriately, as is proposed by institutions of other countries and intergovernmental institutions related to official statistics. The results of the study are presented to show that the implementation of the requirements of the existing and mostly non-AI/ML-specific frames of reference such as law and quality is already sufficient to adequately address the ethical issues based on risk scenarios.
    Release date: 2025-09-08
  • 2. The Future of National Statistical Organisations: The Longer-Term Role and Shape of NSOs Archived
    Articles and reports: 11-522-X202500100024
    Description: This paper explores a vision for the future of National Statistics Offices (NSOs). It analyses the history and role of NSOs before exploring current and future challenges and opportunities for NSOs, before finally outlining a future where NSOs become more agile, open, and collaborative while maintaining their high level of trust in the community, thereby allowing them to fulfil their new role as data stewards in a rapidly evolving data landscape.
    Release date: 2025-09-08
  • 3. One-Stop-Shop for Artificial Intelligence and Machine Learning for Official Statistics Archived
    Articles and reports: 11-522-X202500100027
    Description: Several challenges encountered when constructing U.S. administrative record-based (AR-based) population estimates for 2020 are identified. They include locational accuracy, person coverage and its consistency over time, filtering out non-residents and people not alive on the reference date, uncovering missing links across person and address records, and predicting demographic characteristics. Several ways to address these issues are discussed. Regression results illustrate how the challenges and solutions affect the AR-based county population estimates.
    Release date: 2025-09-08
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  • 1. On the Interplay of Legal Requirements, Quality Aspects and Ethical Risks when using Machine Learning in German Official Statistics Archived
    Articles and reports: 11-522-X202500100014
    Description: Artificial intelligence (AI) with its subfield machine learning (ML) has found its way into administration in general and also into official statistics in Germany in particular. This paper highlights the ethical issues that may arise when using AI/ML in official statistics and examines whether a separate ethical framework is needed to deal with these issues appropriately, as is proposed by institutions of other countries and intergovernmental institutions related to official statistics. The results of the study are presented to show that the implementation of the requirements of the existing and mostly non-AI/ML-specific frames of reference such as law and quality is already sufficient to adequately address the ethical issues based on risk scenarios.
    Release date: 2025-09-08
  • 2. The Future of National Statistical Organisations: The Longer-Term Role and Shape of NSOs Archived
    Articles and reports: 11-522-X202500100024
    Description: This paper explores a vision for the future of National Statistics Offices (NSOs). It analyses the history and role of NSOs before exploring current and future challenges and opportunities for NSOs, before finally outlining a future where NSOs become more agile, open, and collaborative while maintaining their high level of trust in the community, thereby allowing them to fulfil their new role as data stewards in a rapidly evolving data landscape.
    Release date: 2025-09-08
  • 3. One-Stop-Shop for Artificial Intelligence and Machine Learning for Official Statistics Archived
    Articles and reports: 11-522-X202500100027
    Description: Several challenges encountered when constructing U.S. administrative record-based (AR-based) population estimates for 2020 are identified. They include locational accuracy, person coverage and its consistency over time, filtering out non-residents and people not alive on the reference date, uncovering missing links across person and address records, and predicting demographic characteristics. Several ways to address these issues are discussed. Regression results illustrate how the challenges and solutions affect the AR-based county population estimates.
    Release date: 2025-09-08
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