The correct answer is to retrain the model on a balanced dataset, implement explainability features, and require human approval for high-risk recommendations to elderly clients. This solution directly addresses unethical AI recommendations by tackling the root cause—biased training data that exploited elderly clients’ lower likelihood of complaining—while adding transparency and accountability through explainability tools like SHAP values and a human-in-the-loop approval process. On the Salesforce AI Associate exam, this scenario tests your understanding of how reinforcement learning can amplify historical biases when fairness constraints are absent, a common trap where candidates focus only on adding oversight without fixing the data. Remember the three-pillar fix: debias the data, explain the output, and mandate human review for vulnerable groups. A useful memory tip is “Data, Explain, Approve”—the D-E-A sequence for ethical AI remediation.
AI Associate Ethical Considerations of AI Practice Question
This AI Associate practice question tests your understanding of ethical considerations of ai. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 financial institution deploys an AI system to recommend investment portfolios to retail clients. The system uses reinforcement learning to maximize returns based on client risk profiles. After six months, an internal audit reveals that the system has been consistently recommending high-risk, high-commission products to elderly clients with low risk tolerance, resulting in significant financial losses for those clients. The system's training data included historical transactions, which showed that elderly clients were less likely to complain or switch advisors. The institution's AI ethics policy mandates fairness, transparency, and accountability. The system currently provides no explanations for its recommendations, and there is no human oversight process. The compliance team needs to remediate the situation. Which course of action BEST addresses the ethical violations?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Disable the AI system and revert to manual portfolio management.
Why wrong: Manual management is not scalable and ignores the root cause.
B
Add a disclaimer to all recommendations stating that past performance does not guarantee future results.
Why wrong: A disclaimer does not prevent biased recommendations.
C
Adjust the model to lower the risk threshold for all clients.
Why wrong: This may reduce risk but does not address the targeted bias against elderly clients.
D
Retrain the model on a balanced dataset, implement explainability features, and require human approval for high-risk recommendations to elderly clients.
This addresses bias, transparency, and accountability.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Retrain the model on a balanced dataset, implement explainability features, and require human approval for high-risk recommendations to elderly clients.
Option D is correct because it directly addresses the root cause of the ethical violations: biased training data (historical transactions where elderly clients were less likely to complain) and lack of transparency. Retraining on a balanced dataset mitigates the reinforcement learning model's exploitation of that bias, while explainability features (e.g., SHAP values or LIME) and human-in-the-loop approval for high-risk recommendations ensure accountability and fairness as mandated by the AI ethics policy.
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.
✗
Disable the AI system and revert to manual portfolio management.
Why it's wrong here
Manual management is not scalable and ignores the root cause.
✗
Add a disclaimer to all recommendations stating that past performance does not guarantee future results.
Why it's wrong here
A disclaimer does not prevent biased recommendations.
✗
Adjust the model to lower the risk threshold for all clients.
Why it's wrong here
This may reduce risk but does not address the targeted bias against elderly clients.
✓
Retrain the model on a balanced dataset, implement explainability features, and require human approval for high-risk recommendations to elderly clients.
Why this is correct
This addresses bias, transparency, and accountability.
Clue confirmation
The clue word "best" in the question point toward this answer.
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 a single technical fix (like lowering risk thresholds or adding disclaimers) is sufficient to resolve ethical violations, when in fact a multi-pronged approach addressing data bias, transparency, and human oversight is required.
Detailed technical explanation
How to think about this question
In reinforcement learning, the reward function drives behavior; if the training data encodes a bias (e.g., elderly clients rarely complain), the agent learns to maximize returns by targeting that group with high-commission products, as it perceives no negative feedback. Retraining on a balanced dataset requires reweighting or resampling historical transactions to remove the spurious correlation, while explainability techniques like SHAP (SHapley Additive exPlanations) provide per-instance feature importance, enabling auditors to verify that recommendations align with stated risk profiles. Human-in-the-loop approval for high-risk recommendations introduces a fail-safe that prevents automated exploitation of vulnerable groups, aligning with regulatory frameworks like the EU AI Act's requirements for high-risk AI systems.
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: Retrain the model on a balanced dataset, implement explainability features, and require human approval for high-risk recommendations to elderly clients. — Option D is correct because it directly addresses the root cause of the ethical violations: biased training data (historical transactions where elderly clients were less likely to complain) and lack of transparency. Retraining on a balanced dataset mitigates the reinforcement learning model's exploitation of that bias, while explainability features (e.g., SHAP values or LIME) and human-in-the-loop approval for high-risk recommendations ensure accountability and fairness as mandated by the AI ethics policy.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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