- A
Restrict chatbot to only predefined responses.
Why wrong: Restricting to predefined responses limits the chatbot's utility and does not address the underlying bias.
- B
Increase model complexity.
Why wrong: Increasing model complexity often exacerbates bias rather than mitigating it.
- C
Remove all demographic data from training.
Why wrong: Simply removing demographic data may not eliminate bias and could lose important context.
- D
Conduct an AI ethics audit.
An AI ethics audit systematically identifies root causes of bias and establishes a baseline for mitigation.
Question 660 of 1,000
First Step to Address AI 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 company deployed an AI chatbot to handle customer service. The chatbot sometimes generates responses that are biased against certain demographics. The company wants to mitigate this. What is the best first step?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"first"Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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
✓
Conduct an AI ethics audit.
Option D is correct because conducting an AI ethics audit is the best first step to systematically identify the root causes of bias and establish a baseline for mitigation actions. Option A is wrong because restricting the chatbot to only predefined responses limits its utility and does not address the underlying bias. Option B is wrong because increasing model complexity often amplifies existing biases rather than mitigating them. Option C is wrong because simply removing all demographic data from training does not guarantee elimination of bias and may remove valuable context needed for fair predictions.
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.
- ✗
Restrict chatbot to only predefined responses.
Why it's wrong here
Restricting to predefined responses limits the chatbot's utility and does not address the underlying bias.
- ✗
Increase model complexity.
Why it's wrong here
Increasing model complexity often exacerbates bias rather than mitigating it.
- ✗
Remove all demographic data from training.
Why it's wrong here
Simply removing demographic data may not eliminate bias and could lose important context.
- ✓
Conduct an AI ethics audit.
Why this is correct
An AI ethics audit systematically identifies root causes of bias and establishes a baseline for mitigation.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
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: Conduct an AI ethics audit. — Option D is correct because conducting an AI ethics audit is the best first step to systematically identify the root causes of bias and establish a baseline for mitigation actions. Option A is wrong because restricting the chatbot to only predefined responses limits its utility and does not address the underlying bias. Option B is wrong because increasing model complexity often amplifies existing biases rather than mitigating them. Option C is wrong because simply removing all demographic data from training does not guarantee elimination of bias and may remove valuable context needed for fair predictions.
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.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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
2 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. A company uses an AI model to screen job candidates. They discover the model is rejecting candidates from certain zip codes. What should they do first?
medium- A.Increase model complexity
- B.Add more features to the model
- C.Remove zip code feature
- ✓ D.Audit training data for bias
Why D: Auditing the training data for bias is the first step to identify and mitigate unfairness. Option A is wrong because increasing model complexity may exacerbate bias. Option B is wrong because adding more features without bias analysis could introduce more bias. Option C is wrong because removing the zip code feature alone does not address underlying bias in other correlated features.
Variation 2. A company is deploying an AI-powered chatbot for customer service. The chatbot is trained on historical support tickets. Which ethical consideration is MOST important to address before deployment?
easy- A.Minimizing the cost of AI training
- B.Ensuring the chatbot responds quickly to all queries
- ✓ C.Checking for biased or discriminatory patterns in training data
- D.Planning for regular model retraining
Why C: Option C is correct because historical data may contain biased responses, leading to unfair treatment of customers. Option A is wrong because cost is a business consideration, not ethical. Option B is wrong while performance is important, it is secondary to fairness. Option D is wrong because maintenance is operational.
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Last reviewed: Jun 23, 2026
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