- A
Transparency
Why wrong: Transparency is about explaining decisions, not performance equity.
- B
Fairness
Unequal performance across groups violates fairness.
- C
Accountability
Why wrong: Accountability is about who is responsible, not the ethical breach itself.
- D
Privacy
Why wrong: Privacy is not directly related to group performance disparity.
Question 986 of 1,000
AI Associate Ethical Considerations of AI Practice Question
This AI Associate practice question tests your understanding of ethical considerations of ai. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. 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.
An AI system used for medical diagnosis has been shown to have lower accuracy for certain ethnic groups. The development team is considering releasing it anyway because most patients are from the majority group. Which ethical principle is most compromised?
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 scenario describes an AI system that performs worse for certain ethnic groups, yet the team plans to release it anyway because the majority group is unaffected. This directly violates the principle of fairness, which requires that AI systems do not discriminate or perpetuate bias against any group. Releasing a model with known accuracy disparities without mitigation prioritizes overall performance over equitable treatment, compromising fairness.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Transparency
Why it's wrong here
Transparency is about explaining decisions, not performance equity.
- ✓
Fairness
Why this is correct
Unequal performance across groups violates fairness.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Accountability
Why it's wrong here
Accountability is about who is responsible, not the ethical breach itself.
- ✗
Privacy
Why it's wrong here
Privacy is not directly related to group performance disparity.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests fairness by presenting a scenario where a model performs well overall but has known disparities for a subgroup, tempting candidates to choose transparency or accountability because they focus on the team's decision to release rather than the core ethical violation of unequal treatment.
Detailed technical explanation
How to think about this question
Fairness in AI is often operationalized through metrics like demographic parity, equal opportunity, or equalized odds, which measure whether model performance (e.g., accuracy, false positive rate) is consistent across demographic groups. In medical diagnosis, a model with lower accuracy for a minority group could lead to misdiagnosis or delayed treatment, exacerbating health disparities. Real-world examples include commercial health risk prediction algorithms that were found to systematically underestimate the health needs of Black patients, leading to unequal care allocation.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
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
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
Visual reference
What to study next
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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 — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Fairness — The scenario describes an AI system that performs worse for certain ethnic groups, yet the team plans to release it anyway because the majority group is unaffected. This directly violates the principle of fairness, which requires that AI systems do not discriminate or perpetuate bias against any group. Releasing a model with known accuracy disparities without mitigation prioritizes overall performance over equitable treatment, compromising fairness.
What should I do if I get this AI Associate question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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