Algorithm-Assisted Inference and the Future of Official Statistics

Artificial intelligence is entering official statistics through automated coding, imputation, nowcasting from alternative data sources, and small area estimation. This article examines AI adoption through the lens of methodological transitions in survey sampling—from design-based inference through model-assisted estimation—and addresses three questions. First, what is the relationship between algorithm-assisted and model-assisted inference? We show that generalized difference estimators can incorporate machine learning predictions in the same way they incorporate parametric working models. Second, what quality framework extensions are needed for operational deployment? Drawing on vignettes from European and North American statistical offices, we identify five areas requiring development: training data documentation, algorithmic transparency, validation protocols, uncertainty characterization, and reproducibility. We argue for prequential evaluation—assessing calibration and stability over successive production cycles—as an operational practice suited to algorithm-assisted systems. Third, what institutional challenges distinguish AI from earlier transitions? We examine the public-private asymmetry in AI development: whereas twentieth-century methodological innovations emerged largely from public institutions, contemporary AI capabilities are concentrated in private technology companies. European Statistical System initiatives illustrate governance responses, but dependency risks persist. We conclude that algorithm-assisted inference can succeed, but only if it can be made auditable, reproducible, and publicly defensible.