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.
A company is designing an AI system to screen job applicants. To ensure fairness, which practice should be implemented?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Conduct regular fairness audits on model outcomes
Regular fairness audits are essential because they systematically evaluate model outcomes for bias across demographic groups, using metrics like disparate impact or equal opportunity difference. This practice aligns with responsible AI frameworks (e.g., NIST AI Risk Management Framework) and helps detect subtle biases that may emerge from proxy variables or data drift, ensuring the screening process remains equitable over time.
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.
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Use only one data source for consistency
Why it's wrong here
Relying on one source can embed bias; diverse data is better.
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Maximize the model's accuracy on historical hiring decisions
Why it's wrong here
Historical decisions may be biased, so maximizing accuracy perpetuates bias.
✓
Conduct regular fairness audits on model outcomes
Why this is correct
Audits help detect and address disparate impact.
Related concept
Read the scenario before looking for a memorised answer.
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Remove all demographic data from the training set
Why it's wrong here
Removing demographic data does not remove bias; proxies may remain.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the misconception that removing demographic data (option D) is sufficient to ensure fairness, when in reality proxy variables and model behavior must be actively monitored through audits.
Detailed technical explanation
How to think about this question
Fairness audits often involve computing metrics like demographic parity (equal selection rates across groups) or equalized odds (equal false positive/negative rates). Under the hood, these audits require intersectional analysis (e.g., race × gender) because bias may only appear in subgroups. A real-world scenario: Amazon's scrapped hiring tool exhibited gender bias despite removing explicit gender data, because it learned patterns from resumes (e.g., 'women's' vs 'men's' activities) — a regular audit with proxy detection could have flagged this earlier.
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.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Conduct regular fairness audits on model outcomes — Regular fairness audits are essential because they systematically evaluate model outcomes for bias across demographic groups, using metrics like disparate impact or equal opportunity difference. This practice aligns with responsible AI frameworks (e.g., NIST AI Risk Management Framework) and helps detect subtle biases that may emerge from proxy variables or data drift, ensuring the screening process remains equitable over time.
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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Question Discussion
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