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 data scientist is training a model to predict customer churn. To ensure fairness, what should the data scientist do?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Ensure the training data is representative of the entire customer base.
Option B is correct because ensuring the training data is representative of the entire customer base directly addresses fairness by preventing underrepresentation or overrepresentation of specific demographic groups. A representative dataset helps the model learn unbiased patterns across all segments, reducing the risk of disparate impact. This aligns with the principle of fairness in AI, where the model's predictions should not systematically disadvantage any group.
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.
✗
Focus solely on model accuracy ignoring demographic groups.
Why it's wrong here
Ignoring groups can miss fairness issues.
✓
Ensure the training data is representative of the entire customer base.
Why this is correct
Representative data reduces the risk of bias.
Related concept
Read the scenario before looking for a memorised answer.
✗
Remove all demographic attributes from the dataset.
Why it's wrong here
Removing attributes may not address proxy variables.
✗
Use only historical data without checking for bias.
Why it's wrong here
Historical data may encode past biases.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the misconception that simply removing sensitive attributes (like race or gender) is sufficient to ensure fairness, when in reality the model can still learn proxies for those attributes from other correlated features.
Detailed technical explanation
How to think about this question
Under the hood, fairness in machine learning often involves techniques like disparate impact analysis (e.g., the 80% rule) and equalized odds. Even when sensitive attributes are removed, models can infer them via correlated features (e.g., income, education, or location), a subtle behavior known as 'proxy discrimination.' In a real-world scenario, a churn model trained on non-representative data might incorrectly flag a high proportion of a particular demographic as high-risk, leading to unfair targeting of retention offers.
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 small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
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: Ensure the training data is representative of the entire customer base. — Option B is correct because ensuring the training data is representative of the entire customer base directly addresses fairness by preventing underrepresentation or overrepresentation of specific demographic groups. A representative dataset helps the model learn unbiased patterns across all segments, reducing the risk of disparate impact. This aligns with the principle of fairness in AI, where the model's predictions should not systematically disadvantage any group.
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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