Resources
Curated guides, checklists, references and starting points for responsible AI in official statistics. These resources are intentionally practical and can be expanded into fuller guides over time.
Responsible AI checklist for official statistics
Start with purpose, data quality, privacy, bias, transparency, validation, human oversight, documentation and public trust.
Related topicSource-grounded monitoring
Track AI developments through original sources, methodological documentation, institutional guidance and reproducible notes.
Read the monitorQuality assurance template
A future place for documenting assumptions, validation checks, known limitations and operational controls.
Related methodRSS subscription
Follow new updates on AI tools, methods and governance for official statistics.
Subscribe via RSSEvidence and governance
Responsible AI and quality assurance
AAPOR 81st Conference, IAOS Vilnius, and AI Governance in Official Statistics
The AAPOR 81st Annual Conference underscored the cautious integration of LLMs in survey research, emphasizing responsible frameworks for translation...
AI Chatbot Integrity, AAPOR 2026 and AI Day for Federal Statistics
Key words: AI, survey research, official statistics, machine learning, data quality, household surveys, chatbot detection, AAPOR, LLM, questionnaire...
AI Survey Quality Beyond Traditional Safeguards
Key words: AI, survey research, official statistics, machine learning, data quality, household surveys
AI Systems, Data Quality and Bias Risks
Key words: AI, survey research, data quality, household surveys, data analysis
AI Readiness Assessment Toolkit to help NSOs benchmark their progress
Key words: AI, survey research, official statistics, machine learning, data quality, automation, household surveys, data analysis