The answer is to incorporate human review for high-stakes decisions and to conduct regular auditing for adverse impact. These practices are essential because AI models in hiring can perpetuate historical biases through statistical disparities, and auditing using methods like the four-fifths rule—which flags selection rates for protected groups below 80% of the most-selected group—helps detect such adverse impact before it harms candidates. On the Salesforce AI Associate exam, this concept tests your understanding of ethical AI governance under the “Responsible AI” domain, often appearing as a scenario where an automated system screens resumes and you must choose oversight mechanisms over purely technical fixes. A common trap is selecting “remove all human review for efficiency” or “only audit after complaints,” but the exam emphasizes proactive, continuous auditing paired with human judgment for high-risk decisions. Memory tip: think “Audit and Appeal”—always audit for adverse impact and keep a human in the loop for final calls.
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.
Which TWO practices are recommended when using AI for automated decision-making in hiring?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Regularly audit the model for adverse impact.
Option B is correct because regular auditing for adverse impact is a core ethical practice to detect and mitigate bias in AI-driven hiring systems. Audits involve statistical analysis (e.g., the four-fifths rule from the Uniform Guidelines on Employee Selection Procedures) to compare selection rates across protected groups, ensuring the model does not disproportionately disadvantage certain demographics.
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.
✗
Use the AI model as the sole decision-maker.
Why it's wrong here
Sole reliance on AI can be unfair.
✓
Regularly audit the model for adverse impact.
Why this is correct
Auditing detects bias.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use all available data including protected attributes.
Why it's wrong here
Using protected attributes may cause bias.
✓
Incorporate human review for high-stakes decisions.
Why this is correct
Human oversight ensures fairness.
Related concept
Read the scenario before looking for a memorised answer.
✗
Ignore adverse impact if the model is accurate.
Why it's wrong here
Accuracy does not justify adverse impact.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the misconception that model accuracy alone justifies automated decisions, tempting candidates to pick 'Ignore adverse impact if the model is accurate' (Option E) without recognizing that fairness and ethical compliance are separate, non-negotiable requirements.
Detailed technical explanation
How to think about this question
Under the hood, adverse impact audits often use the 'four-fifths rule' (80% rule) from the EEOC's Uniform Guidelines, where a selection rate for a protected group less than 80% of the group with the highest rate indicates potential discrimination. Real-world scenarios, such as Amazon's scrapped AI recruiting tool, show that models trained on historical data can learn biased patterns (e.g., penalizing resumes containing 'women's' words), and only systematic auditing can catch such subtle, systemic biases before deployment.
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.
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
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 small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Regularly audit the model for adverse impact. — Option B is correct because regular auditing for adverse impact is a core ethical practice to detect and mitigate bias in AI-driven hiring systems. Audits involve statistical analysis (e.g., the four-fifths rule from the Uniform Guidelines on Employee Selection Procedures) to compare selection rates across protected groups, ensuring the model does not disproportionately disadvantage certain demographics.
What should I do if I get this AI Associate question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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 →
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 social media platform uses an AI model to automatically detect and remove hate speech. The model uses natural language processing and was trained on public posts. Recently, an internal audit reveals that the model removes posts from minority ethnic groups at a rate 3 times higher than from majority groups, even when the content is similar. The model achieves high precision and recall on the test set. The platform's content moderation team is overwhelmed with appeals. The company wants to maintain a safe environment while being fair. Which approach best addresses both goals?
hard
A.Disable the AI moderation and rely solely on user reports.
✓ B.Conduct an audit of the training data to identify gaps, then retrain with more representative data including diverse examples of hate speech and non-hate speech.
C.Add more human moderators to review all flagged content from minority groups.
D.Adjust the detection threshold only for minority group posts to reduce flags.
Why B: Option B is correct because a comprehensive audit and retraining with diverse data addresses the bias at the root. Option A gives special treatment that could be seen as unfair. Option C removes moderation, risking harmful content. Option D does not solve the underlying bias.
Variation 2. An AI system used for recruitment has been found to be biased. Which THREE steps should be taken to address this? (Choose three.)
hard
A.Deploy the model without changes
✓ B.Audit the training data for bias
✓ C.Retrain the model with a balanced dataset
D.Remove demographic data from the model
✓ E.Monitor outcomes for disparate impact
Why B: Auditing training data, retraining with balanced data, and monitoring outcomes are essential corrective actions.
Last reviewed: Jun 30, 2026
Question Discussion
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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.
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