AI Associate Ethical Considerations of AI Practice Question
A sales team uses Einstein Lead Scoring. They notice the model gives low scores to leads from certain industries. The AI Associate suspects bias. What should they do to validate?
⚠ Common exam trap
Watch out — candidates often confuse model accuracy metrics (like holdout tests) with fairness validation, not realizing that a model can be accurate yet systematically biased against certain subgroups.
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
✓
Analyze the distribution of scores across industry segments.
Analyzing the distribution of scores across industry segments directly validates whether the model exhibits systematic bias. By comparing score distributions, the associate can identify if certain industries are consistently under-scored, which would indicate a biased pattern rather than random variation. This approach aligns with ethical AI practices that require transparency and fairness assessment before any model adjustments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Run a holdout test to check prediction accuracy.
Why it's wrong here
Accuracy test does not directly address bias.
- ✗
Retrain the model with balanced data.
Why it's wrong here
Retraining is a fix, not a validation step.
- ✗
Review the model's confidence intervals.
Why it's wrong here
Confidence intervals measure uncertainty, not bias.
- ✓
Analyze the distribution of scores across industry segments.
Why this is correct
This reveals if certain groups are systematically scored lower.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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