AIF-C01 Guidelines for Responsible AI Practice Question
A large e-commerce company uses a recommendation system to suggest products to customers. Recently, a data scientist noticed that the model's recommendations for high-value luxury items are predominantly shown to users in affluent zip codes, while users in less affluent areas rarely see these items, even if they have searched for them. The company is concerned about fairness and wants to ensure all customers have equal access to recommendations regardless of location. The current model uses collaborative filtering on historical purchase data. The team needs to modify the system without sacrificing overall recommendation accuracy. Which action best addresses the fairness concern while maintaining performance?
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
✓
Remove zip code and any income-correlated features from the training data
The most effective approach is to ensure the model does not use zip code or any feature correlated with income as a direct or indirect input. This removes the proxy for socioeconomic status. Simply equalizing recommendation frequency artificially may hurt relevance. Personalizing based on search history is already being done but zip code bias remains. Adding more training data may not help if the bias is in the features.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Randomly show luxury recommendations to a subset of users regardless of their behavior
Why it's wrong here
Random shows would reduce relevance and customer satisfaction.
- ✓
Remove zip code and any income-correlated features from the training data
Why this is correct
Removing biased features eliminates the source of unfairness in recommendations.
- ✗
Add more training data from less affluent areas to balance the dataset
Why it's wrong here
Adding data may help but the model may still learn biased patterns from zip code.
- ✗
Implement a separate recommendation pipeline for luxury items based only on search history
Why it's wrong here
This does not address the underlying bias; zip code may still influence via other features.
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