AI Associate Ethical Considerations of AI Practice Question
Which THREE factors should be considered when evaluating the fairness of an AI model?
⚠ Common exam trap
Salesforce often tests the distinction between performance metrics (like accuracy) and fairness metrics, trapping candidates who assume a high-accuracy model is automatically fair.
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
✓
Disparate impact ratio across groups.
The disparate impact ratio measures whether an AI model's predictions disproportionately harm or benefit certain demographic groups, typically by comparing selection rates across groups. A ratio below 0.8 or above 1.25 is often considered evidence of adverse impact, making it a key quantitative fairness metric.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Disparate impact ratio across groups.
Why this is correct
Measures adverse impact ratio.
- ✗
Overall accuracy on the test set.
Why it's wrong here
Accuracy does not reflect group fairness.
- ✗
Model training time.
Why it's wrong here
Training time is unrelated to fairness.
- ✓
Equal opportunity difference.
Why this is correct
Equal opportunity ensures similar true positive rates.
- ✓
Demographic parity in predictions.
Why this is correct
Demographic parity requires equal prediction rates across groups.
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