Canonical lesson
Study the complete conceptual, statistical and governance framework.
Open PDF ↗OFFICIAL STATISTICS & AI · OS-A02
AI Concepts for Official Statisticians
Build a rigorous working language for artificial intelligence, connect methods to statistical tasks, and decide where AI assistance is appropriate, testable and professionally accountable.
01 / LEARNING OUTCOME
Classify the task before selecting the technology.
Explain major AI concepts in statistically meaningful terms; distinguish prediction, classification, clustering, language and generative tasks; and specify the evidence, controls and human judgement required before operational use.
02 / AI DECISION FRAMEWORK
Connect six analytical questions before adopting an AI method.
- 01
Statistical task
Define the decision, output and intended use before discussing algorithms.
- 02
Learning paradigm
Distinguish supervised, unsupervised, generative and hybrid approaches.
- 03
Evidence boundary
Identify the population, data-generating process and limits of generalization.
- 04
Performance
Select evaluation criteria that reflect statistical quality and operational risk.
- 05
Assurance
Test robustness, uncertainty, drift, reproducibility and failure behaviour.
- 06
Accountability
Assign human review, escalation and final professional responsibility.
03 / VIDEO LESSON
AI Concepts for Official Statisticians
Follow Module OS-A02 to understand AI mechanisms, evidence requirements, statistical assurance and accountable professional decisions.
Open the lesson on YouTube ↗04 / LEARNING RESOURCES
Public resources for study, practice and executable analysis.
Learner-facing materials are provided from the controlled OS-A02 release. Facilitator guidance, explained solutions, assessor materials and institutional tools remain outside this public resource set.
Executive brief
Review the leadership implications and institutional decisions.
Open PDF ↗Learner guide
Prepare for structured study, reflection and applied work.
Open PDF ↗Lesson presentation
Review the visual lesson with professional narration notes.
Open PDF ↗Editable presentation
Download the PowerPoint presentation with speaker notes.
Download PPTX ↗Executable classification laboratory
Classify statistical tasks and examine bounded AI opportunities.
Download notebook ↗Laboratory README
Follow the environment, execution and interpretation instructions.
Open guide ↗Python requirements
Install the controlled laboratory dependencies.
Open requirements ↗Task-classification data
Inspect the synthetic task evidence used in the laboratory.
Open CSV ↗Stress-test data
Evaluate classification stability under difficult cases.
Open CSV ↗Practice and case packet
Apply the concepts to realistic official-statistics decisions.
Open PDF ↗Learner assessment brief
Document evidence of conceptual and professional competence.
Open PDF ↗05 / PATHWAY CONTINUITY
Conceptual clarity is the second control in responsible AI adoption.
OS-A01 established the institutional information ecosystem. OS-A02 now provides the task language needed to evaluate AI proposals, communicate with technical teams and preserve statistical responsibility.
Return to the pathway →