Quick Answer
The answer is the fairness principle in AI ethics. This is correct because the model’s underrepresentation of certain ethnic groups in its training data directly produces disparate false negative rates, leading to unjust outcomes for those groups—a clear violation of fairness, which requires that AI systems avoid bias and treat all groups equitably. On the Salesforce AI Associate exam, this scenario tests your ability to distinguish fairness from other principles like transparency, accountability, or privacy; a common trap is confusing fairness with transparency, but remember that fairness concerns outcome disparity, not explainability. A helpful memory tip is to think of fairness as “equal treatment across groups”—if the model fails one group more than others, fairness is the principle at stake.
This AI Associate practice question tests your understanding of ethical considerations of ai. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 healthcare company uses an AI model built on Salesforce to predict patient readmission risk. The model is trained on historical data that underrepresents certain ethnic groups. During testing, the model shows significantly higher false negative rates for those groups, meaning it fails to flag high-risk patients. The ethical concern is most directly related to which AI principle?
Privacy
Why wrong: Privacy concerns data protection, not bias or fairness in model outcomes.
Accountability
Why wrong: Accountability addresses who is responsible for AI decisions, not the bias in predictions.
Transparency
Why wrong: Transparency involves explaining how decisions are made, not the fairness of outcomes.
Fairness
Fairness ensures AI does not discriminate against groups; the model's bias is a fairness issue.
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
The correct answer is C because the model's underrepresentation leads to unfair outcomes for specific groups, violating the principle of fairness. Option A is wrong because transparency is about explainability, not outcome disparity. Option B is wrong because accountability refers to who is responsible, not the bias itself. Option D is wrong because privacy is about data protection, not 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.
Privacy
Why it's wrong here
Privacy concerns data protection, not bias or fairness in model outcomes.
Accountability
Why it's wrong here
Accountability addresses who is responsible for AI decisions, not the bias in predictions.
Transparency
Why it's wrong here
Transparency involves explaining how decisions are made, not the fairness of outcomes.
Fairness
Why this is correct
Fairness ensures AI does not discriminate against groups; the model's bias is a fairness issue.
Related concept
Static NAT maps one inside address to one outside address.
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.
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
506 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
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise AI Associate questions linked to AI Fundamentals.
Practise AI Associate questions linked to AI Capabilities in CRM.
Practise AI Associate questions linked to Ethical Considerations of AI.
Practise AI Associate questions linked to Data for AI.
Practise AI Associate questions linked to AI Associate fundamentals.
Practise AI Associate questions linked to AI Associate scenario.
Practise AI Associate questions linked to AI Associate troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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 — The correct answer is C because the model's underrepresentation leads to unfair outcomes for specific groups, violating the principle of fairness. Option A is wrong because transparency is about explainability, not outcome disparity. Option B is wrong because accountability refers to who is responsible, not the bias itself. Option D is wrong because privacy is about data protection, not 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.
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 →
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.
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.