AI Associate Ethical Considerations of AI Practice Question
A company is developing an AI system to assist with hiring. Which TWO practices are essential for ethical AI deployment?
⚠ Common exam trap
Salesforce often tests the misconception that removing demographic features (Option B) is sufficient to eliminate bias, when in fact proxy variables and model behavior must be actively monitored through audits (Option C).
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 regular bias audits on model predictions
Regular bias audits are a core ethical practice for AI systems, especially in hiring. These audits involve systematically testing the model's predictions across demographic groups to detect and mitigate unintended discrimination, ensuring compliance with fairness standards like the EEOC's Uniform Guidelines on Employee Selection Procedures.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Optimize the system for speed to reduce waiting times
Why it's wrong here
Speed is a performance metric, not an ethical consideration.
- ✗
Remove all demographic features to ensure fairness
Why it's wrong here
Removing features does not eliminate bias; proxies may remain.
- ✓
Conduct regular bias audits on model predictions
Why this is correct
Audits help detect and mitigate discriminatory outcomes.
- ✗
Maximize accuracy on historical hiring data
Why it's wrong here
Historical data may be biased, so maximizing accuracy propagates bias.
- ✓
Obtain informed consent from applicants if their data is used
Why this is correct
Consent is a fundamental ethical requirement for data use.
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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.