- A
Remove zip code from the feature set and retrain.
Why wrong: Other features may still correlate with race.
- B
Replace zip code with more relevant non-discriminatory features and retrain with fairness constraints.
Targeted feature engineering and fairness constraints mitigate bias.
- C
Keep zip code but add a fairness penalty to the loss function.
Why wrong: Retaining proxy may still cause disparate impact.
- D
Increase transparency by publishing the model's decision criteria.
Why wrong: Transparency does not correct bias.
Question 86 of 1,000
Bias Mitigation Strategies — Best Course of Action for Proxy Bias
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 credit scoring AI uses 50 features including zip code, age, and income. The model has high accuracy but denies credit disproportionately to a protected group. An audit reveals that zip code is a proxy for race. What is the best course of action?
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
✓
Replace zip code with more relevant non-discriminatory features and retrain with fairness constraints.
Option B is correct because replacing biased proxy with more relevant features can maintain accuracy while reducing discrimination. Option A is wrong because simply removing zip code may not eliminate all proxies. Option C is wrong because retraining with same data yields same bias. Option D is wrong because transparency alone doesn't fix bias.
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.
- ✗
Remove zip code from the feature set and retrain.
Why it's wrong here
Other features may still correlate with race.
- ✓
Replace zip code with more relevant non-discriminatory features and retrain with fairness constraints.
Why this is correct
Targeted feature engineering and fairness constraints mitigate bias.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Keep zip code but add a fairness penalty to the loss function.
Why it's wrong here
Retaining proxy may still cause disparate impact.
- ✗
Increase transparency by publishing the model's decision criteria.
Why it's wrong here
Transparency does not correct bias.
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.
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Ethical Considerations of AI — study guide chapter
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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: Replace zip code with more relevant non-discriminatory features and retrain with fairness constraints. — Option B is correct because replacing biased proxy with more relevant features can maintain accuracy while reducing discrimination. Option A is wrong because simply removing zip code may not eliminate all proxies. Option C is wrong because retraining with same data yields same bias. Option D is wrong because transparency alone doesn't fix bias.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more ways this is tested on AI Associate
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. An AI system for hiring is found to have a disparate impact on a protected class. The company is legally required to...
hard- ✓ A.Conduct a bias audit and take corrective action.
- B.Obtain consent from applicants.
- C.Publish the algorithm.
- D.Discontinue use of the system.
Why A: Option A is correct because under many anti-discrimination laws (e.g., US Title VII), disparate impact requires the employer to conduct a bias audit and take corrective action to mitigate the adverse effect. Option B is wrong because obtaining consent does not address the underlying bias or legal obligation. Option C is wrong because publishing the algorithm is not typically a legal requirement; transparency may be encouraged but not mandated. Option D is wrong because discontinuation is not immediately required; corrective action can resolve the issue without removing the system entirely.
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Last reviewed: Jun 22, 2026
This AI Associate practice question is part of Courseiva's free Salesforce certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI Associate exam.
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