- A
Privacy
Why wrong: Incorrect because privacy concerns data protection (e.g., unauthorized access), not outcome disparities. This model's issue is not about data exposure.
- B
Accountability
Why wrong: Incorrect because accountability refers to assigning responsibility for decisions or outcomes, but the primary ethical issue here is the unfair treatment of certain groups, not who is responsible.
- C
Transparency
Why wrong: Incorrect because transparency involves making the model's workings understandable, while the core problem is the biased performance against underrepresented groups, which is a fairness violation.
- D
Fairness
Fairness ensures AI does not discriminate against groups; the model's bias is a fairness issue.
Question 408 of 1,000
AI Associate Fairness in AI Practice Question
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 in AI. 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.
A healthcare company uses an AI model built on Salesforce to predict patient readmission risk. The model is trained on historical data that underrepresents certain ethnic groups. During testing, the model shows significantly higher false negative rates for those groups, meaning it fails to flag high-risk patients. The ethical concern is most directly related to which AI principle?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Fairness
The correct answer is D (Fairness) because the model's underrepresentation of certain ethnic groups leads to higher false negative rates for those groups, resulting in unfair outcomes. Fairness in AI requires that models do not discriminate against specific groups. Option A (Privacy) is incorrect because the issue is not about data protection but about biased predictions. Option B (Accountability) is incorrect because the concern is the bias itself, not who is responsible. Option C (Transparency) is incorrect because transparency relates to explainability, not outcome disparity.
Key principle: Fairness in AI
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
Why it's wrong here
Incorrect because privacy concerns data protection (e.g., unauthorized access), not outcome disparities. This model's issue is not about data exposure.
- ✗
Accountability
Why it's wrong here
Incorrect because accountability refers to assigning responsibility for decisions or outcomes, but the primary ethical issue here is the unfair treatment of certain groups, not who is responsible.
- ✗
Transparency
Why it's wrong here
Incorrect because transparency involves making the model's workings understandable, while the core problem is the biased performance against underrepresented groups, which is a fairness violation.
- ✓
Fairness
Why this is correct
Fairness ensures AI does not discriminate against groups; the model's bias is a fairness issue.
Related concept
Fairness in AI
Common exam traps
Common exam trap: answer the scenario, not the keyword
Trap: Confusing fairness with transparency. Fairness focuses on equitable outcomes across groups, while transparency is about explainability. Here, the disparity in false negatives is a fairness issue, not a transparency one.
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 in AI
- 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 in AI
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 in AI 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 in AI, then practise related AI Associate questions on the same topic to reinforce the concept.
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Ethical Considerations of AI — study guide chapter
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FAQ
Questions learners often ask
What does this AI Associate question test?
Ethical Considerations of AI — This question tests Ethical Considerations of AI — Fairness in AI.
What is the correct answer to this question?
The correct answer is: Fairness — The correct answer is D (Fairness) because the model's underrepresentation of certain ethnic groups leads to higher false negative rates for those groups, resulting in unfair outcomes. Fairness in AI requires that models do not discriminate against specific groups. Option A (Privacy) is incorrect because the issue is not about data protection but about biased predictions. Option B (Accountability) is incorrect because the concern is the bias itself, not who is responsible. Option C (Transparency) is incorrect because transparency relates to explainability, not outcome disparity.
What should I do if I get this AI Associate question wrong?
Review fairness in AI, then practise related AI Associate questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Fairness in AI
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Last reviewed: Jun 23, 2026
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