AI and Data Minimization Audit: Trimming the Training Set

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-creators-ethics-AI-and-data-minimization-audit-r11a3-creators

1. What is the core idea behind "AI and Data Minimization Audit: Trimming the Training Set"?

   1. AI can audit a training dataset against a minimization principle, but the data steward decides what to remove.
   2. Cyber uplift: does it enable attacks that were previously infeasible?
   3. If unsure, just don't share — that's the safest choice.
   4. alternatives

2. Which term best describes a foundational idea in "AI and Data Minimization Audit: Trimming the Training Set"?

   1. training data
   2. data minimization
   3. privacy
   4. data steward

3. A learner studying AI and Data Minimization Audit: Trimming the Training Set would need to understand which concept?

   1. data minimization
   2. privacy
   3. training data
   4. data steward

4. Which of these is directly relevant to AI and Data Minimization Audit: Trimming the Training Set?

   1. data minimization
   2. training data
   3. data steward
   4. privacy

5. Which of the following is a key point about AI and Data Minimization Audit: Trimming the Training Set?

   1. Map each field to a stated model purpose with a necessity rating
   2. Flag identifiers and quasi-identifiers for additional review
   3. Cyber uplift: does it enable attacks that were previously infeasible?
   4. If unsure, just don't share — that's the safest choice.

6. What is one important takeaway from studying AI and Data Minimization Audit: Trimming the Training Set?

   1. Sign off on a dataset modification
   2. Decide that an 'unnecessary' field can actually be deleted (downstream contracts)
   3. Cyber uplift: does it enable attacks that were previously infeasible?
   4. If unsure, just don't share — that's the safest choice.

7. What is the key insight about "Minimization audit" in the context of AI and Data Minimization Audit: Trimming the Training Set?

   1. Cyber uplift: does it enable attacks that were previously infeasible?
   2. If unsure, just don't share — that's the safest choice.
   3. Prompt: for each field in this schema, rate necessity (essential / supporting / unnecessary) against this model purpose.
   4. alternatives

8. What is the key insight about "Necessity is contested" in the context of AI and Data Minimization Audit: Trimming the Training Set?

   1. Cyber uplift: does it enable attacks that were previously infeasible?
   2. If unsure, just don't share — that's the safest choice.
   3. alternatives
   4. Necessity arguments tend to expand. Data minimization requires a steward who pushes back on 'might be useful later' reas…

9. Which statement accurately describes an aspect of AI and Data Minimization Audit: Trimming the Training Set?

   1. AI can audit a training dataset schema against the model's stated purpose and surface fields that may not be necessary.
   2. Cyber uplift: does it enable attacks that were previously infeasible?
   3. If unsure, just don't share — that's the safest choice.
   4. alternatives

10. Which best describes the scope of "AI and Data Minimization Audit: Trimming the Training Set"?

    1. It is unrelated to ethics workflows
    2. It focuses on AI can audit a training dataset against a minimization principle, but the data steward decides what
    3. It applies only to the opposite beginner tier
    4. It was deprecated in 2024 and no longer relevant

11. Which section heading best belongs in a lesson about AI and Data Minimization Audit: Trimming the Training Set?

    1. Cyber uplift: does it enable attacks that were previously infeasible?
    2. If unsure, just don't share — that's the safest choice.
    3. What AI does well here
    4. alternatives

12. Which section heading best belongs in a lesson about AI and Data Minimization Audit: Trimming the Training Set?

    1. Cyber uplift: does it enable attacks that were previously infeasible?
    2. If unsure, just don't share — that's the safest choice.
    3. alternatives
    4. What AI cannot do

13. Which of the following is a concept covered in AI and Data Minimization Audit: Trimming the Training Set?

    1. data minimization
    2. training data
    3. privacy
    4. data steward

14. Which of the following is a concept covered in AI and Data Minimization Audit: Trimming the Training Set?

    1. data minimization
    2. training data
    3. privacy
    4. data steward

15. Which of the following is a concept covered in AI and Data Minimization Audit: Trimming the Training Set?

    1. data minimization
    2. training data
    3. privacy
    4. data steward