AI4OfficialStats: Audit Statistical Fidelity of AI-Mediated Official Statistics
Provides deterministic tools for auditing whether artificial
intelligence systems preserve the numerical, semantic, contextual,
temporal, geographic, unit, provenance, revision, transformation, and
uncertainty properties of official statistics. Structured reference
statistics and machine-generated claims can be compared using
non-compensatory critical-error rules, weakest-link and geometric fidelity
summaries, provenance graphs, and portable SHA-256 proof bundles. The
package provides bounded connectors for official statistical services, an
easy schema-detection and file-import layer for arbitrary official
organisations, extensible provider registries, and a search-first natural-
language verification layer that classifies statistical claims, selects
suitable official sources, retrieves candidate evidence, matches statistical
dimensions, and compares claimed values. If no reference year is stated,
verification uses the latest available matching official observation and
discloses the resolved year. Source attribution is optional:
automatic routing can choose suitable providers when none is named, while
explicitly named supported sources are respected by default. Automatic
catalogue-to-observation verification is implemented for the World Bank,
WHO, the United Nations Statistics Division Sustainable Development Goals
service, and the European Commission statistical service,
while other providers remain available through bounded direct connectors or
generic official-data import. Prompt perturbation,
statistical red-team generation, minimal-pair tests, and benchmark data support
reproducible evaluation of generative, retrieval-augmented, and agentic
statistical systems. An embedded alignment layer maps claim-level controls
to relevant activities of the Generic Statistical Business Process Model
(GSBPM) 5.2, including Analyse, Disseminate, Evaluate, Quality Management,
and Metadata Management.
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