AI for Official Statistics
Authors/Creators
Description
This book exists to help move official statistics beyond the traditional, into applied machine learning, and onto GenAI.
Federal statistical agencies collect, process, and publish some of the most consequential data in the world. The methods behind that work are evolving. Machine learning, large language models, and agentic AI are not future possibilities; they are current tools with real applications in survey operations, data processing, and statistical production.
This book covers what those methods are, how they work, when they apply, and when they do not. Every chapter includes plain-language explanation, practical context for federal operations, and guidance on explaining methods to leadership and stakeholders.
All examples use public datasets from the Census Bureau, Bureau of Labor Statistics, and other federal sources. No proprietary data, no restricted access required. Full runnable code for every chapter is available in the companion repository: https://github.com/brockwebb/ai4stats
Series information (English)
v1.0.1 — Errata and formatting fixes. Added inline citations for foundational methods and frameworks across all 15 chapters. Corrected factual errors identified during post-publication review (NIOCCS naming, CPS sample size, 2020 Census epsilon value, stale Executive Order, and OMB memo references). Removed unsupported empirical claims. Updated SDL risk decision tree figure to remove reference to rescinded OMB M-24-10. Fixed PDF admonition blocks orphaning across page breaks.
Files
AI_for_Official_Statistics_Webb2026_v1.0.1.pdf
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