- A
Re-sample the training data to include more female candidates and use fairness-aware algorithms.
Balancing data and using fairness techniques reduce bias.
- B
Add a post-processing adjustment to increase female candidates' scores.
Why wrong: Post-processing may not address model's internal bias.
- C
Accept the bias as a reflection of historical data.
Why wrong: Historical bias should be actively mitigated, not accepted.
- D
Remove the gender feature from the model.
Why wrong: Removing the feature does not prevent correlation with other features.
Question 219 of 1,000
Mitigating Gender Bias in AI Hiring
This AI Associate practice question tests your understanding of ethical considerations of ai. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. A key principle to apply: fairness-aware algorithms. 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 system recommends job candidates to recruiters. The system was trained on resumes of past successful hires, most of whom were male. As a result, it consistently ranks female candidates lower. What is the most appropriate mitigation?
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
✓
Re-sample the training data to include more female candidates and use fairness-aware algorithms.
Option A is the most appropriate mitigation because it addresses the root cause of bias by ensuring the training data is representative and using algorithms designed to detect and correct unfairness. Re-sampling provides a more balanced dataset, and fairness-aware algorithms actively work to prevent discriminatory outcomes. Option B (post-processing) can help but may not be sufficient alone and does not fix the underlying data bias. Option C (accepting bias) ignores the ethical responsibility. Option D (removing gender) is ineffective because proxy variables like job history or education can still correlate with gender and perpetuate bias.
Key principle: Fairness-aware algorithms
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Re-sample the training data to include more female candidates and use fairness-aware algorithms.
Why this is correct
Balancing data and using fairness techniques reduce bias.
Related concept
Fairness-aware algorithms
- ✗
Add a post-processing adjustment to increase female candidates' scores.
Why it's wrong here
Post-processing may not address model's internal bias.
- ✗
Accept the bias as a reflection of historical data.
Why it's wrong here
Historical bias should be actively mitigated, not accepted.
- ✗
Remove the gender feature from the model.
Why it's wrong here
Removing the feature does not prevent correlation with other features.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates may think removing the sensitive feature (e.g., gender) eliminates bias, but proxy variables can still lead to discrimination.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Fairness-aware algorithms
- Proxy variables
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
Fairness-aware algorithms
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. Fairness-aware algorithms 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.
Review fairness-aware algorithms, then practise related AI Associate questions on the same topic to reinforce the concept.
- →
Ethical Considerations of AI — study guide chapter
Learn the concepts, then practise the questions
- →
Ethical Considerations of AI practice questions
Targeted practice on this topic area only
- →
All AI Associate questions
1,000 questions across all exam domains
- →
Salesforce AI Associate AI Associate study guide
Full concept coverage aligned to exam objectives
- →
AI Associate practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI Associate practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Ethical AI and Data Privacy practice questions
Practise AI Associate questions linked to Ethical AI and Data Privacy.
Salesforce Einstein AI Features practice questions
Practise AI Associate questions linked to Salesforce Einstein AI Features.
AI Fundamentals practice questions
Practise AI Associate questions linked to AI Fundamentals.
AI Capabilities in CRM practice questions
Practise AI Associate questions linked to AI Capabilities in CRM.
Ethical Considerations of AI practice questions
Practise AI Associate questions linked to Ethical Considerations of AI.
Data for AI practice questions
Practise AI Associate questions linked to Data for AI.
AI Associate fundamentals practice questions
Practise AI Associate questions linked to AI Associate fundamentals.
AI Associate scenario practice questions
Practise AI Associate questions linked to AI Associate scenario.
AI Associate troubleshooting practice questions
Practise AI Associate questions linked to AI Associate troubleshooting.
Practice this exam
Start a free AI Associate practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this AI Associate question test?
Ethical Considerations of AI — This question tests Ethical Considerations of AI — Fairness-aware algorithms.
What is the correct answer to this question?
The correct answer is: Re-sample the training data to include more female candidates and use fairness-aware algorithms. — Option A is the most appropriate mitigation because it addresses the root cause of bias by ensuring the training data is representative and using algorithms designed to detect and correct unfairness. Re-sampling provides a more balanced dataset, and fairness-aware algorithms actively work to prevent discriminatory outcomes. Option B (post-processing) can help but may not be sufficient alone and does not fix the underlying data bias. Option C (accepting bias) ignores the ethical responsibility. Option D (removing gender) is ineffective because proxy variables like job history or education can still correlate with gender and perpetuate bias.
What should I do if I get this AI Associate question wrong?
Review fairness-aware algorithms, then practise related AI Associate questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Fairness-aware algorithms
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 organization uses an AI-powered resume screening tool to shortlist candidates for a software engineering role. The tool was trained on historical hiring data from the past five years, during which the company predominantly hired male candidates. After deployment, the tool consistently ranks female candidates lower, even when they have equivalent qualifications. The AI team reports that the overall model accuracy is 92%, and they argue that performance is strong. However, the diversity and inclusion team raises ethical concerns about gender bias. The Salesforce AI Associate is asked to evaluate the situation. What should the associate recommend?
easy- A.Continue using the model because 92% accuracy is acceptable and the bias is not significant.
- ✓ B.Retrain the model using a balanced dataset that includes equal representation from all genders and implement ongoing fairness monitoring.
- C.Replace the current AI tool with a different vendor's tool without further analysis.
- D.Manually adjust the scoring algorithm to give preference to female candidates to balance the outcome.
Why B: Option B is correct because retraining with a balanced dataset addresses the root cause of bias, and ongoing monitoring ensures fairness over time. Option A is incorrect because ignoring ethical concerns for accuracy is unacceptable. Option C is incorrect because switching vendors without understanding the bias may not solve the issue. Option D is incorrect because manually adjusting scores introduces reverse discrimination and is unethical.
Keep practising
More AI Associate practice questions
- An admin wants to compare the AI-generated forecast with a rep's commit forecast to identify gaps. Which feature should…
- A Salesforce admin implements Einstein Bots for customer service. To ensure the bot does not use biased language, what s…
- Which Einstein feature provides automated statistical analysis of Salesforce data, including story creation and improvem…
- A sales operations team wants to improve forecast accuracy by using AI. They currently use manual rollups. Which TWO Ein…
- A sales rep wants to generate a personalized email to a prospect using AI. Which Einstein GPT feature should they use?
- A healthcare company uses Einstein Prediction Builder to predict patient no-shows. After training a model, they receive…
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.