AI Associate Ethical Considerations of AI Practice Question
A credit scoring AI uses 50 features including zip code, age, and income. The model has high accuracy but denies credit disproportionately to a protected group. An audit reveals that zip code is a proxy for race. What is the best course of action?
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
✓
Replace zip code with more relevant non-discriminatory features and retrain with fairness constraints.
Replacing biased proxy with more relevant features can maintain accuracy while reducing discrimination. Option A is wrong because simply removing zip code may not eliminate all proxies. Option C is wrong because retraining with same data yields same bias. Option D is wrong because transparency alone doesn't fix bias.
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 zip code from the feature set and retrain.
Why it's wrong here
Other features may still correlate with race.
- ✓
Replace zip code with more relevant non-discriminatory features and retrain with fairness constraints.
Why this is correct
Targeted feature engineering and fairness constraints mitigate bias.
- ✗
Keep zip code but add a fairness penalty to the loss function.
Why it's wrong here
Retaining proxy may still cause disparate impact.
- ✗
Increase transparency by publishing the model's decision criteria.
Why it's wrong here
Transparency does not correct bias.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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