AI Governance Foundations

Review questions

Integrated summary: AI Governance Foundations

AI governance begins with the system purpose, decision boundary and affected people, followed by clear human responsibility. Probabilistic output requires measurement of error across actual use groups. The team records each claim and its evidence and distinguishes model capability from authorization for a particular use. Data sources, constraints and rights are reviewed before operation. The owner tests the decision point where harm changes and defines when human intervention or path shutdown is required. The unit output is a decision record connecting purpose, risk, owner, evidence and escalation limit, rather than a general statement of governance principles. The unit also tests effective appeal access with a sound denominator and independent sample, closing governance findings only after repair evidence.

Review questions

Test your understanding with these questions. Marking and saved results are available with a free account.

  1. Question 1A team reports 92% overall accuracy for an application triage aid but does not separate errors on urgent applications. What is the strongest step before allowing it to delay reviews?
  2. Question 2The vendor holds urgent-case measurements, while the product owner authorizes use of the triage aid inside the organization. Which allocation is best for release?
  3. Question 3Which metric card best tests control of missed urgent applications?
  4. Question 4A complaint summary becomes automatic closure without a model change. Which gate is required?
  5. Question 5A team is buying a tool to rank urgent complaints; the supplier offers only overall accuracy. What evidence is most relevant first?
  6. Question 6An appeal dashboard counts only requests received. A reviewer finds the appeal button missing in the Arabic interface. What is the first assurance judgment?