AI Recommendation Systems: When Engagement Optimization Harms Users

End-of-lesson check

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

1. What is the main idea of "AI Recommendation Systems: When Engagement Optimization Harms Users"?

   1. Recommendation AI optimized for engagement can promote harmful content. Designing systems that resist this requires deliberate trade-offs.
   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 Recommendation Systems: When Engagement Optimization Harms Users"?

   1. engagement optimization
   2. recommendation AI
   3. harm reduction
   4. trade-offs

3. Which use of AI fits this topic best?

   1. Eliminate all harm without sacrificing some engagement
   2. Let the AI decide what matters without your review
   3. Define harm categories explicitly (self-harm content, eating disorders, extremism) and de-promote them
   4. Use the answer before checking whether it fits the situation

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

   1. Define harm categories explicitly (self-harm content, eating disorders, extremism) and de-promote them
   2. Explain the topic in plain language
   3. Organize a draft for human review
   4. Eliminate all harm without sacrificing some engagement

5. What should a careful learner remember about "Recommendation system harm-reduction audit"?

   1. Use "Recommendation system harm-reduction audit" as a reminder to verify the AI output before anyone relies on it.
   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 AI 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 AI.

9. Which action would help you apply "AI Recommendation Systems: When Engagement Optimization Harms Users" responsibly?

   1. Substitute pure-AI moderation for human judgment on novel cases
   2. Use the tool to avoid thinking through the tradeoff
   3. Keep going even if the output conflicts with a trusted source
   4. Trade off engagement for user wellbeing on identified harm vectors

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

    1. Substitute pure-AI moderation for human judgment on novel cases
    2. Define harm categories explicitly (self-harm content, eating disorders, extremism) and de-promote them
    3. Ask for a plain-language explanation of engagement optimization
    4. Compare the answer with a trusted source