AI Associate Ethical Considerations of AI Practice Question
A data scientist is training a model to predict customer churn. To ensure fairness, what should the data scientist do?
⚠ Common exam trap
Salesforce often tests the misconception that simply removing sensitive attributes (like race or gender) is sufficient to ensure fairness, when in reality the model can still learn proxies for those attributes from other correlated features.
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
✓
Ensure the training data is representative of the entire customer base.
Ensuring the training data is representative of the entire customer base directly addresses fairness by preventing underrepresentation or overrepresentation of specific demographic groups. A representative dataset helps the model learn unbiased patterns across all segments, reducing the risk of disparate impact. This aligns with the principle of fairness in AI, where the model's predictions should not systematically disadvantage any group.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Focus solely on model accuracy ignoring demographic groups.
Why it's wrong here
Ignoring groups can miss fairness issues.
- ✓
Ensure the training data is representative of the entire customer base.
Why this is correct
Representative data reduces the risk of bias.
- ✗
Remove all demographic attributes from the dataset.
Why it's wrong here
Removing attributes may not address proxy variables.
- ✗
Use only historical data without checking for bias.
Why it's wrong here
Historical data may encode past biases.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI Associate practice question is part of Courseiva's free Salesforce certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI Associate exam.