Practical methods for applying AI and machine learning in official statistics while respecting survey design, quality, transparency and governance requirements.

Survey-aware machine learning

Models that account for survey weights, stratification, clusters, nonresponse and population inference.

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Editing, imputation and validation

AI-assisted data editing, anomaly detection, imputation, statistical checks and human review loops.

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Quality assurance

Source-grounded validation, documentation, monitoring, auditability and quality gates for statistical production.

Responsible AI checklist

Reproducible workflows

Versioned analysis, transparent prompts, documented assumptions and replicable data science workflows.

Related tools

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Survey methodology and machine learning

Topic group

Quality methods

Cleaning, editing, imputation and validation