AI Associate Ethical AI and Data Privacy Practice Question
A financial services company is deploying Einstein Prediction Builder to predict loan default risk. They are concerned about using sensitive attributes like race or gender in the model. Which data governance practice should they apply?
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
✓
Exclude sensitive attributes from the model features unless they are essential and legally permitted, and ensure no proxies exist.
Data minimisation is a core principle: only use relevant features for the prediction. Sensitive attributes that could lead to discriminatory decisions should be excluded unless legally required and properly managed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use synthetic data to replace sensitive attributes with random values.
Why it's wrong here
Synthetic data may not preserve real-world correlations and could still leak information.
- ✗
Include all available attributes to maximize model accuracy, then apply fairness constraints.
Why it's wrong here
Including sensitive attributes risk embedding bias even with fairness constraints; minimisation is safer.
- ✗
Mask the sensitive attributes but still include them in the model training.
Why it's wrong here
Masking is not enough; if the attribute is used in training, the model can still learn patterns associated with it.
- ✓
Exclude sensitive attributes from the model features unless they are essential and legally permitted, and ensure no proxies exist.
Why this is correct
Data minimisation dictates excluding unnecessary sensitive data; also check for proxies to avoid indirect discrimination.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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