AI Associate Ethical Considerations of AI Practice Question
A developer is creating a custom AI model on Salesforce. To ensure the model is fair across demographic groups, which activity should be included in the development process?
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
✓
Bias testing using a diverse test dataset.
Bias testing using diverse datasets directly evaluates fairness across groups.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Feature selection using correlation matrix.
Why it's wrong here
Feature selection may help but does not guarantee fairness.
- ✓
Bias testing using a diverse test dataset.
Why this is correct
This evaluates model performance across demographics.
- ✗
Cross-validation to avoid overfitting.
Why it's wrong here
Cross-validation addresses overfitting, not fairness.
- ✗
Hyperparameter tuning with grid search.
Why it's wrong here
Tuning optimizes performance, not fairness.
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