This AI Associate practice question tests your understanding of ethical considerations of ai. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. A key principle to apply: fairness. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Exhibit
Model Audit Results:
- Overall accuracy: 92%
- Accuracy for group A (majority): 95%
- Accuracy for group B (minority): 70%
- False positive rate for group A: 5%
- False positive rate for group B: 20%
- False negative rate for group A: 3%
- False negative rate for group B: 15%
Refer to the exhibit. An AI model audit shows performance differences across demographic groups. Which ethical concern is most critical?
Exhibit
Model Audit Results:
- Overall accuracy: 92%
- Accuracy for group A (majority): 95%
- Accuracy for group B (minority): 70%
- False positive rate for group A: 5%
- False positive rate for group B: 20%
- False negative rate for group A: 3%
- False negative rate for group B: 15%
A
Privacy: the data includes sensitive attributes
Why wrong: Privacy concerns are not directly indicated by the performance differences.
B
Accountability: the audit was not independent
Why wrong: The audit's independence is not mentioned.
C
Transparency: the model's overall accuracy is too low
Why wrong: Overall accuracy is high, but group fairness is lacking, which is a fairness issue, not transparency.
D
Fairness: the model performs worse for the minority group
The model's worse performance for the minority group violates fairness principles.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Fairness: the model performs worse for the minority group
The performance disparity across demographic groups, as shown in the exhibit, directly indicates a fairness issue. Option A is incorrect because privacy concerns are not directly indicated by the performance differences. Option B is incorrect because the audit's independence is not mentioned; the issue is about fairness, not accountability. Option C is incorrect because overall accuracy is high, but group fairness is lacking, which is a fairness issue, not transparency. Option D is correct because the model's worse performance for the minority group violates fairness principles.
Key principle: Fairness
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Privacy: the data includes sensitive attributes
Why it's wrong here
Privacy concerns are not directly indicated by the performance differences.
✗
Accountability: the audit was not independent
Why it's wrong here
The audit's independence is not mentioned.
✗
Transparency: the model's overall accuracy is too low
Why it's wrong here
Overall accuracy is high, but group fairness is lacking, which is a fairness issue, not transparency.
✓
Fairness: the model performs worse for the minority group
Why this is correct
The model's worse performance for the minority group violates fairness principles.
Related concept
Fairness
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often incorrectly select privacy when they see demographic data, but the key issue here is the performance disparity, not data collection.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
Fairness
Bias
Disparate Impact
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Fairness
Real-world example
How this comes up in practice
A practitioner preparing for the AI Associate exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Fairness Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Review fairness, then practise related AI Associate questions on the same topic to reinforce the concept.
Ethical Considerations of AI — This question tests Ethical Considerations of AI — Fairness.
What is the correct answer to this question?
The correct answer is: Fairness: the model performs worse for the minority group — The performance disparity across demographic groups, as shown in the exhibit, directly indicates a fairness issue. Option A is incorrect because privacy concerns are not directly indicated by the performance differences. Option B is incorrect because the audit's independence is not mentioned; the issue is about fairness, not accountability. Option C is incorrect because overall accuracy is high, but group fairness is lacking, which is a fairness issue, not transparency. Option D is correct because the model's worse performance for the minority group violates fairness principles.
What should I do if I get this AI Associate question wrong?
Review fairness, then practise related AI Associate questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Fairness
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Question Discussion
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