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Session 3

When Artificial Intelligence Meets Official Statistics

3 June 2026
11:00 – 12:30
ŠIBENIK IV

Presentation title
Artificial Intelligence in Official Statistics: Opportunities and Quality Risks for Sustaining User Trust
National statistical offices (NSOs) are under increasing pressure to respond to growing user expectations for timely, relevant, and high-quality statistical information.

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At the same time, the volume, variety, and complexity of data sources continue to expand. In this context, advanced digital technologies—particularly artificial intelligence (AI)—are gaining attention as promising tools for modernizing statistical production systems. AI-based methods are already being introduced across various stages of the statistical value chain, including data collection, classification, editing, imputation, and validation, with the objective of enhancing the efficiency, consistency, and scalability of official statistics.

Despite these potential benefits, the use of AI in official statistics also raises important concerns that directly relate to statistical quality and user trust. Issues such as transparency of algorithms, explainability of results, algorithmic bias, reproducibility, and accountability pose new challenges for statistical institutions that traditionally rely on well-documented and methodologically transparent processes. If not properly addressed, these challenges may weaken confidence in official statistics, even when technical performance improves.

This paper examines the implications of adopting AI-based methods for the quality framework of official statistics and explores how these implications affect the trust of data users. The analysis is based on a systematic review of academic literature, international guidelines, and institutional reports, complemented by selected examples from NSOs that have begun to integrate AI and machine learning into their production processes. For instance, the Office for National Statistics in the United Kingdom has piloted AI tools, such as generative AI-based classifiers, to automate the coding of survey responses and process receipt data for its Living Costs and Food Survey, which has reduced processing time and improved coding accuracy. The review considers both the opportunities offered by AI—such as faster processing times, reduced manual workload, and enhanced capacity to handle complex data—and the risks that may affect key quality dimensions, including accuracy, transparency, coherence, and interpretability.

The findings suggest that while AI can strengthen certain aspects of quality, particularly efficiency and timeliness, it also introduces new vulnerabilities related to governance, ethical oversight, and methodological transparency. The paper argues that AI should not be viewed as a standalone technical solution, but rather as a component that must be carefully embedded within existing statistical quality frameworks. Aligning AI applications with established quality principles, ethical standards, and robust governance mechanisms is essential to ensure that innovation supports, rather than undermines, the credibility and long-term trustworthiness of official statistics.

Main author / Presenter
Bassant Ali
Central Agency for Public Mobilization and Statistics

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Bassant Mahmoud Ali has been working in Central Agency for Public Mobilization and Statistics (CAPMAS) since 2012 as a coordinator and translator in the General Department of International Cooperation. My role involves translating official correspondence, providing simultaneous interpretation during meetings with international organizations, organizing official visits, representing CAPMAS at governmental and international events, and fostering institutional partnerships. I earned an MBA from ESLSCA University in Egypt in 2024, a Master of Arts in Women's Leadership from Mississippi University for Women in 2020, and a Bachelor of Arts in Spanish from Ain Shams University. Currently enrolled in the DBA program at Helwan University in Egypt. I have previous experience in quality control and customer service at Raya Holding Company in Egypt. Fluent in Arabic, English, and Spanish. I have completed numerous professional courses in statistics, public policy, and leadership.

Presentation title
Leveraging AI to Enhance Engagement for Istat: Survey Respondent, User Support and Natural Language Access to SDMX Data
In 2025, the Italian National Institute of Statistics (Istat) introduced artificial intelligence across several of its user facing systems to enhance interaction with external audiences, both in the context of survey participation and in the dissemination of statistical information.

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Within this broader digital innovation effort, two AI driven solutions were developed and deployed. The first concerns the Istat Contact Centre, where an AI based virtual assistant, through natural language interaction, was integrated to support respondents (providing guidance on survey participation) and data users (helping retrieve information). Trained on an internal, curated knowledge base and subjected to extensive pre release testing—including accuracy checks, UX assessments, and off topic controls—the system was authorized for deployment only after exceeding a 95% accuracy threshold. Since its introduction, it has managed more than 8,000 interactions, monitored through a dedicated dashboard that analyses conversation quality and supports continuous refinement through human in the loop supervision. The assistant is currently available on the Contact Centre webpage (for users and respondents) and on the Enterprises portal (for respondents), with planned extensions to additional entry points and to the telephone channel for automated call routing and classification. Continuous monitoring and iterative improvements ensure high reliability, reduce hallucinations, and strengthen compliance with institutional requirements related to accuracy, transparency, and user protection. The second initiative involved the integration of an AI assistant into IstatData, the Institute’s SDMX based data warehouse for official aggregate statistics. Instead of a generative conversational agent, Istat developed a “tool based AI assistant” comprising two components: a natural language search assistant and a copilot for customizing tables, charts, and maps. Operating exclusively on interface controls—never on the underlying data—the system transforms stringent constraints stemming from the EU AI Act, data integrity requirements, and institutional trust obligations into the backbone of a robust architecture. A multi stage validation process, including large scale simulated testing and a hierarchical retrieval algorithm, minimized semantic drift and virtually eliminated hallucinations, ensuring full methodological traceability and user control. The paper presents the methodological, infrastructural, and governance aspects of these initiatives, highlighting their regulatory and ethical implications. Together, they illustrate how AI can be responsibly integrated into public statistical services to improve usability, accessibility, and operational efficiency while preserving the rigor, transparency, and reliability that characterize official statistics.

Main author / Presenter
Salvatore Agrillo
ISTAT

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Salvatore Agrillo is a Computer Engineer, he works at ISTAT within the DCIT — the Directorate for Information Technology and Communication Systems. He is passionate about his profession and continuously deepen his expertise in artificial intelligence. His focus is on developing AI-driven solutions that simplify the work of both operators and end users. His job is also his greatest passion, so even in the free time he enjoys designing new tools, sometimes useful in everyday life.

CO-AUTHORS:

Presentation title
Unlocking Legacy Statistics with Large Language Models
Within the framework of Essnet AIML4OS, Work Package 12, National Statistical Institutes (NSIs) are increasingly exploring the use of large language models (LLMs) to improve efficiency, accessibility, and quality across statistical production and dissemination processes.

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In June 2025, a multi-country hackathon was organised, bringing together experts from six European countries: Portugal, the Netherlands, Sweden, Ireland, Norway, and France, to collaboratively explore practical, quality-focused applications of LLMs in official statistics. The initiative aimed to promote cross-country knowledge exchange, assess feasibility under real institutional and governance constraints, and develop testable prototypes that contribute to a shared methodological foundation for the responsible adoption of LLMs in official statistics.

The hackathon focused on key quality-related dimensions that are highly relevant for National Statistical Institutes (NSIs), including efficiency gains, reusability, data accessibility, on-premise compatibility, robustness of evaluation, feasibility, and long-term sustainability. The prototype presented in this paper, Dissemination Summary, was developed by the Portuguese team and addresses quality enhancement in statistical dissemination. It automatically generates concise, multilingual summaries of statistical reports published 20 to 30 years ago. Although these reports were carefully prepared, they are typically lengthy, with relevant information dispersed throughout the text, and the underlying data were not structured in database formats, as modern dissemination infrastructures did not yet exist at the time. As a result, retrieving and reusing this information today is time-consuming and costly.

The proposed approach leverages generates structured summaries enriched with keywords and thematic tags. Given the critical importance of validation, transparency, trustworthiness, and traceability in official statistics, every numerical value included in the generated summaries is accompanied by an explicit reference indicating its exact location in the original document, allowing users to directly verify the source of each figure.

Built on a retrieval-augmented generation (RAG) workflow, it ingests PDF reports authored by subject-matter experts, applies vector embeddings and LLM prompting, and outputs standardized summaries in machine-readable formats like JSON or Markdown. The design supports both local and external LLM deployments, aligning with institutional requirements for data confidentiality and on-premise compatibility. Evaluation across the predefined quality dimensions indicated high scores for reusability, feasibility, data accessibility, and lifespan, highlighting the need for further work on evaluation robustness.

This contribution demonstrates how collaborative, prototype-driven experimentation can translate emerging AI technologies into concrete quality improvements for official statistics. It offers practical insights into balancing innovation with institutional constraints and providing roadmap for integrating LLM-based tools into quality management frameworks within statistical organizations.

Main author / Presenter
Luis Ferreira
Instituto Nacional de Estatística

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Luis Ferreira holds a degree in Informatics. Has been working at Statistics Portugal (INE) since 2000, within the Methodological and Information Systems Department, where he focuses on the development and design of applications to support surveys and data collection.

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Presentation title
The imperative of quality : how is AI changing the deal? perspective and practices from the OECD.
Quality has long been the cornerstone of official statistics, underpinning trust, relevance and international comparability.

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As artificial intelligence (AI) becomes increasingly embedded in the production, dissemination and use of official statistics, it is reshaping not only statistical processes, but also the very conditions under which quality is defined, assessed and governed. This paper examines how AI is changing the “quality deal” from the perspective of the OECD, drawing on the new 2026 OECD Statistics and Data Quality Framework and on emerging practices across OECD statistical activities.

The paper is structured in two complementary parts. The first part takes a conceptual perspective, exploring how AI challenges and reconfigures established quality dimensions and trade-offs. Building on the 2026 Framework, it examines how core sub-dimensions such as accuracy, timeliness, coherence, transparency, reproducibility and interpretability are affected when AI systems are introduced into statistical value chains. While AI can enhance quality by enabling automated validation, anomaly detection, bias monitoring and continuous process optimisation, it also raises new risks related to opacity, model drift, dependence on external data or vendors, and blurred lines of accountability. Rather than replacing existing quality concepts, the paper argues that AI requires them to be applied more explicitly, consistently and system-wide, with stronger emphasis on governance, documentation and human oversight.

The second part focuses on OECD practices and evidence, drawing on concrete examples of how AI is being used to support, stress or transform quality management. It reviews early experiences with AI-enabled quality assurance, production support and dissemination tools, highlighting where AI has demonstrably improved quality outcomes and where it has introduced new vulnerabilities or uncertainties. These experiences illustrate that AI can be both a lever for strengthening quality and a source of systemic risk if not carefully governed.

Finally, the paper addresses an emerging blind spot: user perceptions of quality in an AI-mediated statistical ecosystem. As official statistics are increasingly accessed, interpreted and reused through AI systems, statistical organisations have limited visibility on how quality is perceived and operationalised by users—human or machine. The paper highlights the need for renewed user research, experimentation and collective learning to better understand what “quality” means in practice in an AI-mediated world, and how official statistics can remain authoritative, trustworthy and fit for purpose under these conditions.

Main author / Presenter
Francois Fonteneau
OECD

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François Fonteneau has 20 years of international experience in innovation applied to the production and use of statistics. François works at the Organisation for Economic Co-operation and Development (OECD). He currently holds a dual role as Senior Advisor on Artificial Intelligence (AI) and data quality to the OECD Chief Statistician, and as Coordinator of AI-focused projects with the Partnership in Statistics for Development in the 21st Century (PARIS21). In 2024, François worked on measuring private and public investment in AI, publishing the OECD’s first methodology and estimates on the subject. Previously, he served as Deputy Head at PARIS21. François also worked for the Food and Agriculture Organization of the United Nations (FAO) on surveys and censuses methodology and implementation, as well as open data. François holds a Masters Degree in Agriculture Economics from INA-PG, France and a Masters Degree in Econometrics from ENSAE, France.

Presentation title
Leveraging AI and GIS for Official Statistics: Case Studies, Risks, and Considerations for Quality
Quality is the foundation of trust in official statistics, and the integration of AI and Geographic Information Systems (GIS) offers a practical path to increasing accuracy, insights, and the public's use of official statistics.

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This presentation will demonstrate how deep learning tools can be applied to satellite imagery, flagging changes and updating building records- ultimately providing a new data source to augment field data collection and the use of administrative records. We will present use cases from NSOs around the world where AI and GIS have effectively improved coverage, timeliness, and consistency. At the same time, we will address the risks that accompany increased reliance on imagery, including scene misclassification, temporal drift, and model bias. We outline mitigation strategies such as rigorous ground-truthing, stratified sampling, uncertainty quantification, and continuous quality assurance to safeguard output integrity. Beyond imagery, geospatial machine learning methods help identify patterns and anomalies, verify the quality of collected data, and elevate the reliability of statistical outputs. Incorporating geospatial context in analysis reveals geographic dynamics that are otherwise hidden, sharpening decision-making and resource allocation. Finally, AI- and GIS-enabled dissemination tools —through interactive, map-based interfaces— make statistics more transparent and accessible, strengthening user understanding and confidence in official statistics. Taken together, these practices navigate opportunities and risks so that AI and GIS enhance quality and sustain trust in official statistics.

Main author / Presenter
Kate Hess
Esri Inc

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Kate Hess is Esri's Business Development Manager for Official Statistics, based in New York City. She supports national statistics offices globally, promoting the use of GIS and remote sensing to modernize census operations, data analysis and dissemination. Previously, Kate spent 5 years as a Senior Solution Engineer at Esri working as a technical specialist with NSOs, UN agencies, and supporting efforts toward the UN Sustainable Development Goals. Before joining Esri, she worked with the NASA DEVELOP program, using satellite data to study climate change impacts, and supported the NASA Global Ecosystem Dynamics Investigation (GEDI) mission as a master's student at the University of Maryland. Her specialties include applications of GeoAI and extracting information from imagery, the integration of statistical and geospatial data, field data collection and operations, and GIS data dissemination/analysis applications.