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 model for predicting employee performance is found to have a higher false positive rate for women than for men. What is the best course of action?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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 and retrain the model to reduce bias
Option A is correct because a higher false positive rate for women indicates the model has learned biased patterns from the training data, likely due to imbalanced or skewed historical data. Investigating the cause—such as examining feature correlations, data distribution, and model architecture—allows for targeted retraining (e.g., reweighting, adversarial debiasing, or fairness constraints) to reduce bias without sacrificing overall performance. This aligns with ethical AI principles and regulatory expectations, ensuring the model is fair across demographic groups.
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.
✓
Investigate the cause and retrain the model to reduce bias
Why this is correct
Retraining with fairness constraints mitigates bias.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Lower the decision threshold for women to equalize false positive rates
Why it's wrong here
Adjusting thresholds without addressing model bias is not a true fix.
✗
Proceed with deployment because the overall accuracy is acceptable
Why it's wrong here
High overall accuracy can mask disparate impact.
✗
Use the model but require manual review for all female candidates
Why it's wrong here
Manual review is inefficient and may introduce human bias.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the misconception that adjusting thresholds or adding manual review can fix bias, when in fact these are superficial patches that do not address the root cause in the model's training data or architecture.
Detailed technical explanation
How to think about this question
Under the hood, bias often stems from proxy features (e.g., zip code correlating with race) or label imbalance in training data. Techniques like reweighing training samples, using adversarial networks to remove protected attribute information from latent representations, or applying fairness metrics (e.g., demographic parity, equal opportunity) during training can mitigate bias. In a real-world scenario, a hiring model trained on past promotion data may learn that women are less likely to be promoted due to historical discrimination, leading to higher false positives for women when predicting future performance.
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: Investigate the cause and retrain the model to reduce bias — Option A is correct because a higher false positive rate for women indicates the model has learned biased patterns from the training data, likely due to imbalanced or skewed historical data. Investigating the cause—such as examining feature correlations, data distribution, and model architecture—allows for targeted retraining (e.g., reweighting, adversarial debiasing, or fairness constraints) to reduce bias without sacrificing overall performance. This aligns with ethical AI principles and regulatory expectations, ensuring the model is fair across demographic groups.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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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