AI Bias That Hurt Real People

End-of-lesson check

8 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-builders-ethics-safety-ai-bias-hurt

1. What is the main idea of "AI Bias That Hurt Real People"?

   1. AI bias isn't just a theory.
   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 Bias That Hurt Real People"?

   1. disparate impact
   2. algorithmic bias
   3. wrongful arrest
   4. audit

3. Which use of AI fits this topic best?

   1. Let the AI decide what matters without your review
   2. Use the answer before checking whether it fits the situation
   3. When AI is used on you, ask what data it was trained on
   4. Use the first answer without checking it

4. What should a careful learner remember about "Where bias hits hardest"?

   1. Use AI to draft or organize ideas about algorithmic bias, 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

5. 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

6. How should AI output about algorithmic bias 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

7. Name one way to verify an AI answer about algorithmic bias.

8. Which action would help you apply "AI Bias That Hurt Real People" responsibly?

   1. Use the tool to avoid thinking through the tradeoff
   2. Keep going even if the output conflicts with a trusted source
   3. Use the first answer without checking it
   4. Push for the right to a human review