AI High-Stakes Recommendation Audits: Reviewing What the Model Suggested

End-of-lesson check

10 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-ethics-safety-high-stakes-recommendation-audit-r8a4-adults

1. What is the main idea of "AI High-Stakes Recommendation Audits: Reviewing What the Model Suggested"?

   1. AI can audit its own recommendation history for patterns, but the decision to override or retrain belongs to humans.
   2. Use AI as the final authority for the whole decision
   3. Avoid checking the answer once it sounds polished
   4. Focus only on speed instead of judgment

2. Which concept is most central to "AI High-Stakes Recommendation Audits: Reviewing What the Model Suggested"?

   1. high-stakes decisions
   2. recommendation audit
   3. review loop
   4. accountability

3. Which use of AI fits this topic best?

   1. Decide if a disparate pattern is justified by the underlying decision context
   2. Let the AI decide what matters without your review
   3. Cluster recommendations by outcome category and disparity dimension
   4. Use the answer before checking whether it fits the situation

4. Which limitation should you watch for in this topic?

   1. Cluster recommendations by outcome category and disparity dimension
   2. Explain the topic in plain language
   3. Organize a draft for human review
   4. Decide if a disparate pattern is justified by the underlying decision context

5. What should a careful learner remember about "Sample-and-explain pass"?

   1. Use AI to draft or organize ideas about recommendation audit, then verify before acting.
   2. Skip the context so the tool can guess faster
   3. Treat the output as private even after sharing it online
   4. Use the answer without checking the source

6. You want to use AI after this lesson. What is the safest next step?

   1. Act immediately because the AI answer is written clearly
   2. AI cannot make the human values or safety decision for you.
   3. Hide uncertainty so the final answer looks cleaner
   4. Use private or sensitive details before checking permission

7. How should AI output about recommendation audit be treated?

   1. As proof that no other source is needed
   2. As a replacement for context, consent, or expert review
   3. As a draft or helper output that still needs human judgment and verification
   4. As something that becomes correct when it sounds confident

8. Name one way to verify an AI answer about recommendation audit.

9. Which action would help you apply "AI High-Stakes Recommendation Audits: Reviewing What the Model Suggested" responsibly?

   1. Authorize a model rollback or policy change
   2. Use the tool to avoid thinking through the tradeoff
   3. Keep going even if the output conflicts with a trusted source
   4. Generate the questions a human reviewer should ask each cluster

10. Which choice is a bad use of AI for this lesson?

    1. Authorize a model rollback or policy change
    2. Cluster recommendations by outcome category and disparity dimension
    3. Ask for a plain-language explanation of high-stakes decisions
    4. Compare the answer with a trusted source