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 uses Salesforce Einstein to build an AI model that predicts customer churn. The model is trained on historical data from the past two years. During testing, the model shows significantly higher accuracy for male customers compared to female customers. What is the most ethical course of action?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Investigate the cause of the disparity, retrain the model with more representative data, and re-evaluate fairness.
Option B is correct because investigating and retraining the model to reduce bias is the ethical approach. Option A is wrong because ignoring the disparity could lead to unfair treatment. Option C is wrong because immediately deploying without correction is irresponsible. Option D is wrong because manually adjusting predictions introduces new biases.
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.
✗
Deploy the model but add a disclaimer that it may be less accurate for female customers.
Why it's wrong here
A disclaimer does not mitigate the actual bias in decisions.
✗
Deploy the model as is, because it still meets the overall accuracy threshold.
Why it's wrong here
This ignores the ethical issue of disparate impact.
✓
Investigate the cause of the disparity, retrain the model with more representative data, and re-evaluate fairness.
Why this is correct
This addresses bias and upholds fairness principles.
Related concept
Read the scenario before looking for a memorised answer.
✗
Manually adjust the model's output to ensure equal churn predictions across genders.
Why it's wrong here
Manual adjustments are not a reliable or transparent fix.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 AI Associate exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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: Investigate the cause of the disparity, retrain the model with more representative data, and re-evaluate fairness. — Option B is correct because investigating and retraining the model to reduce bias is the ethical approach. Option A is wrong because ignoring the disparity could lead to unfair treatment. Option C is wrong because immediately deploying without correction is irresponsible. Option D is wrong because manually adjusting predictions introduces new biases.
What should I do if I get this AI Associate question wrong?
Identify which AI Associate exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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