The correct answer is to retrain the model with a balanced dataset that includes more female candidates and remove gender-correlated features. This directly addresses the root cause of the gender bias in AI resume screening by ensuring the training data reflects a fair representation of all applicants, while stripping out proxy variables like graduation years or extracurricular activities that correlate with gender. On the Salesforce AI Associate exam, this scenario tests your understanding of bias mitigation in machine learning pipelines, specifically how historical hiring data can encode systemic discrimination. A common trap is choosing to simply adjust the shortlist ratio manually, which masks the bias without fixing the model. Remember the memory tip: “Bias lives in the data, not the output—fix the training set, not the result set.”
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 HR department uses an AI tool to screen resumes for a software engineering position. The tool was trained on resumes of past successful hires, who were predominantly male. The tool has been in use for three months, during which only 10% of candidates shortlisted for interviews are female, even though 40% of applicants are female. The hiring managers are satisfied with the quality of candidates shortlisted, as most perform well in interviews. However, the company's diversity and inclusion officer raises an ethical concern. What should the company do to address this bias?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Retrain the model with a balanced dataset that includes more female candidates and remove gender-correlated features.
Option B is correct because retraining with a balanced dataset and using gender-blind features directly addresses the source of bias. Option A perpetuates the bias. Option C is too labor-intensive for high volume. Option D still relies on the biased model for initial screening.
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.
✓
Retrain the model with a balanced dataset that includes more female candidates and remove gender-correlated features.
Why this is correct
This reduces bias by ensuring the model learns from diverse examples and avoids proxy discrimination.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use the AI tool for initial screening but allow candidates to appeal the decision.
Why it's wrong here
Appeals may help individual cases but do not address the systematic bias in the model.
✗
Continue using the current tool since it selects high-quality candidates.
Why it's wrong here
The tool is perpetuating gender bias, which is unethical and may violate employment laws.
✗
Manually review all resumes without using the AI tool.
Why it's wrong here
Manual review is time-consuming, expensive, and may still be subject to human 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.
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: Retrain the model with a balanced dataset that includes more female candidates and remove gender-correlated features. — Option B is correct because retraining with a balanced dataset and using gender-blind features directly addresses the source of bias. Option A perpetuates the bias. Option C is too labor-intensive for high volume. Option D still relies on the biased model for initial screening.
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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