AI Associate Ethical Considerations of AI Practice Question
A financial services firm deployed an AI model to automate loan approvals. The model was trained on historical loan data from the past 10 years, which shows that applicants from certain zip codes have higher default rates. After six months, the company's compliance team receives complaints that applicants from predominantly low-income neighborhoods are being rejected at a much higher rate than applicants from affluent areas, even when their financial profiles are similar. The model's overall accuracy remains high (95%), and the loan default rate has decreased by 15% since deployment. The company wants to address the ethical concerns without sacrificing performance. Which course of action should the company take?
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 with a balanced dataset that includes more examples from underrepresented neighborhoods and enforce fairness constraints.
Retraining with balanced data mitigates the representation bias, addressing the root cause. Option A ignores the fairness issue. Option C removes a feature that may be a proxy for other factors, but it may not eliminate bias if other correlated features remain. Option D adjusts thresholds only for some groups, which could be considered unfair and may not be accepted by regulators.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove the zip code feature from the model inputs.
Why it's wrong here
Removing zip code may not eliminate bias if other features (e.g., income, property value) correlate with it.
- ✓
Retrain the model with a balanced dataset that includes more examples from underrepresented neighborhoods and enforce fairness constraints.
Why this is correct
Balanced data reduces bias and fairness constraints ensure equitable treatment, aligning with ethical AI principles.
- ✗
Adjust the approval threshold lower only for applicants from low-income neighborhoods.
Why it's wrong here
Different thresholds for different groups could be seen as discriminatory and may violate equal lending laws.
- ✗
Continue using the existing model since it has high accuracy and reduces defaults.
Why it's wrong here
High accuracy does not guarantee fairness; ignoring disparity can lead to regulatory penalties and reputational damage.
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