Machine learning has rapidly expanded from academic research into operational use across government, finance, healthcare, and industry. Within the public sector, national statistical agencies are increasingly exploring machine learning to improve the efficiency, accuracy, and scalability of statistical production.
This intersection of statistical methodology and artificial intelligence raises significant questions around quality assurance, legal compliance, ethical considerations, and technological infrastructure. Understanding how machine learning integrates into official statistics is relevant for data scientists, methodologists, and policy professionals who work at the boundary of statistics and computing.
About the book
Foundations and Advances of Machine Learning in Official Statistics, edited by Florian Dumpert and published by Springer as an Open Access title in 2025, provides a comprehensive overview of current research and developments at the intersection of machine learning and official statistics.
The book is organized into four thematic sections, addressing methodological aspects, legal and ethical considerations, technological infrastructure, and practical use cases from national statistical offices across Europe. It targets methodologists working in statistical institutions, data scientists, and researchers engaged in responsible machine learning development for public sector applications.
What you will learn
Readers will gain an understanding of how machine learning methods are being adopted and adapted within the context of official statistics. The book explains methodological challenges specific to this domain, including resampling-based performance estimation, interpretable machine learning, and fairness considerations in algorithmic decision-making. It also addresses legal and regulatory implications of using machine learning in statistical production.
On the technological side, readers are introduced to cloud-native data science platforms tailored for statistical agencies. The use case chapters provide concrete examples from real institutional settings, covering automated coding tasks, statistical matching, retrieval-augmented generation pipelines, and large-scale classification systems.
Table of contents
- Machine Learning in Official Statistics: A Preface-Like Introduction
- Part I: Methodological Aspects
- Leveraging Machine Learning for Official Statistics
- Challenges in Resampling-Based Performance Estimation
- Part II: Legal, Ethical, and Quality Aspects
- Quality Dimensions and Quality Guidelines for Machine Learning in Official Statistics
- Interpretable Machine Learning for Official Statistics
- Fairness in Machine Learning for National Statistical Organizations
- Legal Implications for the Use of Machine Learning in Official Statistics
- Part III: Technological Aspects
- A Cloud-Native Data Science Platform for Official Statistics
- Part IV: Use Cases and Insights
- Domain Adaptation of a BERT Model for Analyzing Job Advertisements
- Approaches to Automated NACE Coding of German Business Activity Descriptions
- An Automated Machine Learning Pipeline for Statistical Matching
- Big Data and Machine Learning at Istat
- Streamlining Business Functions in Official Statistical Production with Machine Learning
- Building a Retrieval-Augmented Generation Pipeline to Trace Administrative Data Use
Book details
- Title: Foundations and Advances of Machine Learning in Official Statistics
- Author(s): Florian Dumpert (Editor)
- Main category: Artificial Intelligence
- Subcategory: Machine Learning
- Language: English
- License: Creative Commons License (CC) – Open Access
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