The answer is to test the model for disparate impact across demographic groups, implement a human-in-the-loop for high-stakes decisions, and ensure transparency in how the AI flags transactions. Testing for disparate impact is critical because it directly addresses the ethical concern of algorithmic bias, ensuring the fraud detection system does not unfairly penalize specific demographic groups based on race, gender, or age. This step, combined with a human-in-the-loop, prevents fully automated decisions that could cause financial harm or erode user trust, aligning with Salesforce’s core principles of accountability and fairness. On the Salesforce AI Associate exam, this question tests your understanding of ethical AI governance, often appearing as a scenario where you must choose between purely technical fixes and human oversight. A common trap is selecting only bias detection without the human review component. Remember the mnemonic “Bias, Review, Explain” to recall the three essential steps: test for bias, implement human review, and explain decisions.
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.
An AI system is used to detect fraud in financial transactions. Which THREE steps should be taken to address ethical concerns?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Implement a human-in-the-loop for high-stakes decisions
Option C is correct because implementing a human-in-the-loop ensures that high-stakes decisions, such as blocking a legitimate transaction or allowing a potentially fraudulent one, are reviewed by a human before final action. This addresses ethical concerns by preventing fully automated decisions that could cause financial harm or violate user trust, and it aligns with principles of accountability and fairness in AI governance.
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.
✗
Lower the fraud detection threshold to catch more cases
Why it's wrong here
Lowering thresholds increases false positives and potential harm.
✗
Automatically accept all flagged transactions to improve user experience
Why it's wrong here
Ignoring fraud defeats the purpose.
✓
Implement a human-in-the-loop for high-stakes decisions
Why this is correct
Human oversight ensures accountability.
Related concept
Read the scenario before looking for a memorised answer.
✓
Ensure the model provides explanations for its decisions
Why this is correct
Explainability fosters trust and allows verification.
Related concept
Read the scenario before looking for a memorised answer.
✓
Test the model for disparate impact across demographic groups
Why this is correct
Bias testing prevents discrimination.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the misconception that ethical AI is solely about improving model performance or user experience, when in fact it requires balancing accuracy, fairness, and human oversight—candidates may incorrectly choose options that sound beneficial (like lowering thresholds) without considering the ethical trade-offs.
Detailed technical explanation
How to think about this question
A human-in-the-loop (HITL) system typically uses a confidence threshold: when the model's prediction probability falls below a certain level (e.g., 0.85), the case is escalated to a human reviewer. This balances automation with oversight, and in practice, financial institutions often use a tiered approach where low-risk flags are auto-approved, medium-risk flags are reviewed by junior analysts, and high-risk flags require senior approval. Real-world examples include credit card fraud detection systems where a transaction flagged as high-risk (e.g., over $10,000 in a new location) is held for manual verification before being declined.
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: Implement a human-in-the-loop for high-stakes decisions — Option C is correct because implementing a human-in-the-loop ensures that high-stakes decisions, such as blocking a legitimate transaction or allowing a potentially fraudulent one, are reviewed by a human before final action. This addresses ethical concerns by preventing fully automated decisions that could cause financial harm or violate user trust, and it aligns with principles of accountability and fairness in AI governance.
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 →
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.
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.