AI Associate Ethical Considerations of AI Practice Question
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?
⚠ Common exam trap
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.
Answer choices
Why each option matters
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.
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.
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.
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