Verify and protect before you trust: A practical protocol for AI and algorithmic adoption in methodology development for national statistical offices

To meet increasing user demands for granular demographic and socio-economic indicators under tightening budgets, national statistical offices have to continuously engage in methodology research and development, including harnessing big data, satellite imagery, and transactional sources to improve or redesign data collection instruments. AI can help with that ongoing effort. This paper focuses on using AI-generated algorithms to test, verify, and illustrate a new statistical methodology before it is trusted for production. Trust in the use of AI in official statistics has many dimensions; we deliberately restrict this paper to two pillars: an independent statistical verification step before the algorithm is trusted for production; and disciplined protection of respondent confidentiality throughout development and testing. We illustrate this process of building trust, drawing on the author’s own experience directing AI to build and run the algorithm for a Mini-Max Hierarchical Bayes (HB) sampling method. This paper also gives a checklist that any national statistical office can apply when evaluating an AI-assisted or algorithmic method for production use. The protocol and checklist are cross-checked against two established reference points for methodological practice in official statistics: the UN Fundamental Principles of Official Statistics and the HLG-MOS Quality Framework for Statistical Algorithms.