AI Associate Ethical AI and Data Privacy Practice Question
A financial services firm uses Einstein Discovery to predict loan default risk. To comply with data minimisation principles and avoid using sensitive PII unnecessarily, which TWO actions should the data science team 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
✓
Remove demographic fields such as race and gender from the training dataset
Data minimisation means using only relevant fields. Removing unnecessary PII features and performing feature selection reduces risk. Auditing for bias and enabling toxicity detection are good practices but not directly about minimisation.
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 demographic fields such as race and gender from the training dataset
Why this is correct
Demographic fields are often irrelevant and could introduce bias; removing them aligns with minimisation.
- ✓
Perform feature selection to retain only the most predictive fields
Why this is correct
Feature selection reduces the number of fields, aligning with minimisation.
- ✗
Include all available fields to maximize model accuracy
Why it's wrong here
Including all fields violates minimisation and may include unnecessary PII.
- ✗
Use feature engineering to derive synthetic attributes that correlate with protected attributes
Why it's wrong here
This could reintroduce bias and does not reduce data usage.
- ✗
Enable Einstein Trust Layer's toxicity detection on the model inputs
Why it's wrong here
Toxicity detection is for content safety, not data minimisation.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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