Quick Answer
The correct answer is Fairness, as the scenario describes a clear fairness principle violation in AI. This is because fairness in AI ethics requires that systems do not produce discriminatory outcomes against protected groups, and here the model’s disparate impact on a specific demographic directly contravenes that principle. On the Salesforce AI Associate exam, this concept tests your ability to distinguish fairness from other ethical principles like transparency or accountability, often using hiring or lending scenarios where biased data leads to unequal treatment. A common trap is confusing fairness with accuracy—a model can be highly accurate overall yet still violate fairness by systematically disadvantaging a group. Remember the memory tip: “Fairness is about the outcome, not the algorithm’s intent”—if the result harms a group disproportionately, fairness is broken, regardless of how the model was built.
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.
A company uses an AI model to screen job applicants. They discover the model is rejecting candidates from a certain demographic at a higher rate. Which ethical principle is most clearly violated?
Fairness
Correct. The model's bias against a demographic violates fairness.
Transparency
Why wrong: Transparency is about openness, but the core issue here is bias.
Privacy
Why wrong: Privacy concerns data handling, not demographic bias.
Accountability
Why wrong: Accountability is about responsibility, not directly the bias itself.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Fairness
Fairness requires that AI systems do not discriminate against groups. The model's disparate impact violates fairness.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Fairness
Why this is correct
Correct. The model's bias against a demographic violates fairness.
Related concept
Static NAT maps one inside address to one outside address.
Transparency
Why it's wrong here
Transparency is about openness, but the core issue here is bias.
Privacy
Why it's wrong here
Privacy concerns data handling, not demographic bias.
Accountability
Why it's wrong here
Accountability is about responsibility, not directly the bias itself.
Common exam traps
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
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
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI Associate NAT questions on configuration and troubleshooting.
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FAQ
Ethical Considerations of AI — This question tests Ethical Considerations of AI — Static NAT maps one inside address to one outside address..
The correct answer is: Fairness — Fairness requires that AI systems do not discriminate against groups. The model's disparate impact violates fairness.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI Associate NAT questions on configuration and troubleshooting.
Static NAT maps one inside address to one outside address.
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Last reviewed: Jun 23, 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.
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