What Is AI in Official Statistics? A Practical Guide for Saudi Arabia and the GCC

Quick answer: AI in official statistics means using machine learning and large language models to support the work of national statistical offices: coding occupations and economic activities, conducting and checking survey interviews, detecting errors, filling gaps and explaining results to users. Used well, it makes statistics faster and more consistent. It does not replace statistical methodology, quality frameworks or the confidentiality of respondents.

I have spent more than 20 years producing labour force statistics: first as Director of Labour Force Statistics at the National Statistical Institute of Tunisia, and since 2021 supporting the General Authority for Statistics (GASTAT) in Saudi Arabia on the national Labour Force Survey. Over the last few years, the question I hear most from statisticians in the region is no longer "should we use AI?" but "where do we start, and how do we keep quality under control?" This guide is my practical answer.

What does "AI in official statistics" actually mean?

Official statistics are the figures that governments publish to describe the economy and society: population, employment and unemployment, prices, trade, health, education. They are produced by national statistical offices following international standards and strict rules on quality and confidentiality.

AI in this context is not a new kind of statistic. It is a set of tools that help with specific steps of the statistical production process:

• Machine learning (ML): models trained on past data to classify, predict or detect anomalies.

• Large language models (LLMs): models that read and write natural language, useful for text answers, documentation and user questions.

• Speech technologies: speech-to-text and text-to-speech, which make voice-based interviews possible.

Where can AI help in the statistical production process?

The Generic Statistical Business Process Model (GSBPM) describes how statistics are produced, from design to dissemination. AI can add value at almost every phase. These are the use cases I consider most mature for statistical offices in the GCC and MENA:

1. Automatic coding. Assigning occupation codes (ISCO-08) and economic activity codes (ISIC Rev.4) to free-text answers is slow and inconsistent when done manually. ML and LLM-based classifiers can propose codes with a confidence score, leaving experts to review only the difficult cases.

2. Survey data collection. AI can assist interviewers in real time, check answers for consistency during the interview, and even conduct structured interviews by voice. I describe this approach in my articles on AI-assisted interviews (AIAI) and speech-to-speech surveys.

3. Editing and data validation. Anomaly detection models flag unusual records (for example, an implausible combination of age, education and occupation) so that reviewers focus on real problems.

4. Imputation and estimation. ML methods can complement classical imputation for missing values and help combine survey data with administrative registers, as long as the methods are documented and tested.

5. Dissemination and user support. LLM-based assistants can help users find the right indicator, explain definitions such as the unemployment rate or labour force participation rate, and answer questions in both Arabic and English.

Why does this matter for Saudi Arabia, the GCC and MENA?

Statistical offices in the region face a specific combination of challenges and opportunities:

• Demand for timely data is growing. National transformation programmes such as Saudi Vision 2030 rely on frequent, detailed indicators to monitor progress, especially on the labour market.

• Labour markets are complex. Large non-national workforces, rapid changes in female participation and fast-growing new sectors make classification and measurement harder.

• Two languages. Questionnaires, open-ended answers and publications are often in Arabic and English. AI tools must work well in Arabic, which is not always the case for tools designed elsewhere.

• Strong digital infrastructure and AI strategies. Several GCC countries have national data and AI authorities and data protection laws, such as the Saudi Data and AI Authority (SDAIA) and the Personal Data Protection Law in Saudi Arabia. These create both an enabling environment and clear obligations.

What are the risks, and how do statistical offices manage them?

Official statistics are trusted because they are accurate, impartial and confidential. AI must not weaken any of these. The main risks I see are:

• Confidentiality: sending respondents' answers to external AI services can breach statistical law and data protection rules. Sensitive processing should run in controlled environments.

• Bias: a model trained on past data can reproduce past errors, for example by systematically mis-coding certain occupations or groups.

• Transparency: users and auditors must be able to understand how a figure was produced. "The model decided" is not an acceptable methodology note.

• Quality drift: a model that works today may degrade as the economy changes. It needs monitoring, like any other statistical process.

International work, such as the machine learning and large language model projects of the UNECE High-Level Group for the Modernisation of Official Statistics (HLG-MOS), offers useful frameworks for managing these risks.

How should a statistical office start with AI?

From my experience with labour force surveys, the offices that succeed follow a few simple rules:

1. Start with one well-defined, repetitive task, such as occupation coding, where results can be measured against expert coding.

2. Keep a human in the loop. Let the model propose and the expert decide, at least until accuracy is proven.

3. Measure quality with the same rigour as any other method: accuracy, consistency over time and impact on published indicators.

4. Protect the data first. Decide where models run and what data they may see before choosing a tool.

5. Document everything and publish the methodology, so users can trust the results.

6. Invest in people. Statisticians who understand both survey methodology and AI are the real bottleneck. Training them, for example in Python and practical AI tools, pays off quickly.

Frequently asked questions

Will AI replace statisticians?

No. AI automates repetitive steps, but defining concepts, designing surveys, validating results and explaining them to decision-makers remain human responsibilities. The role of statisticians shifts toward supervising and validating automated processes.

Can AI be used for the Labour Force Survey?

Yes. The most direct uses are automatic coding of occupation and economic activity, consistency checks during interviews, AI-assisted or voice-based interviewing, and faster production of tables and explanations. Each use must be tested against the survey's quality standards.

Do AI tools work in Arabic?

Increasingly, yes, but performance varies. Any tool must be tested on real Arabic survey answers, including dialect and mixed Arabic-English text, before it is used in production.

Key takeaways

• AI supports specific steps of statistical production; it does not replace statistical methodology.

• The most mature uses are coding, data validation, AI-assisted interviews and user support.

• In Saudi Arabia, the GCC and MENA, Arabic-language performance, confidentiality and data protection law are the first things to get right.

• Start small, keep experts in the loop, measure quality and document everything.

Written by Yamen Helel, statistician and labour force statistics expert supporting official statistics in Saudi Arabia.