AI Associate Ethical Considerations of AI Practice Question
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?
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
✓
Retrain the model using a balanced dataset that includes equal representation from all genders and implement ongoing fairness monitoring.
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Continue using the model because 92% accuracy is acceptable and the bias is not significant.
Why it's wrong here
Even with high accuracy, bias against female candidates is a serious ethical concern; ignoring it perpetuates unfair hiring practices.
- ✓
Retrain the model using a balanced dataset that includes equal representation from all genders and implement ongoing fairness monitoring.
Why this is correct
This directly addresses the bias by ensuring the training data is representative and includes measures to monitor fairness.
- ✗
Replace the current AI tool with a different vendor's tool without further analysis.
Why it's wrong here
Replacing the tool without analyzing the root cause of bias may lead to similar issues; it is not a thorough solution.
- ✗
Manually adjust the scoring algorithm to give preference to female candidates to balance the outcome.
Why it's wrong here
This introduces reverse discrimination and is ethically problematic; it does not address the underlying bias in the model.
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Variation 1. 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?
medium- ✓ A.Re-sample the training data to include more female candidates and use fairness-aware algorithms.
- B.Add a post-processing adjustment to increase female candidates' scores.
- C.Accept the bias as a reflection of historical data.
- D.Remove the gender feature from the model.
Why A: 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.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.