AI Associate Ethical AI and Data Privacy Practice Question
A sales operations manager wants to use Einstein Lead Scoring to prioritize leads. They have historical data showing that leads from a certain postal code have a low conversion rate. However, they suspect the low conversion is due to a past marketing campaign that was poorly targeted, not the demographics of that area. What is the BEST way to ensure the AI model does not unfairly penalize leads from that postal code?
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
✓
Audit the model for disparate impact on that postal code and retrain with updated labels that reflect the true conversion potential
Bias in historical data can lead to unfair predictions. The best approach is to audit the model for bias and retrain with corrected labels or additional features that capture the true drivers of conversion, rather than simply omitting the feature or adjusting scores manually.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Manually increase the lead scores for all leads from that postal code
Why it's wrong here
Manual adjustments are not scalable and may introduce new biases. The model should be corrected at the training data level, not by post-hoc score manipulation.
- ✓
Audit the model for disparate impact on that postal code and retrain with updated labels that reflect the true conversion potential
Why this is correct
Auditing identifies bias; correcting the labels (e.g., re-marketing campaign data as neutral) and retraining addresses the root cause without losing the feature's legitimate predictive power.
- ✗
Remove the postal code field from the model training data entirely
Why it's wrong here
Removing the field may still allow correlated features (e.g., income) to proxy for postal code, and it eliminates potentially useful signal. Better to correct the underlying data bias.
- ✗
Use the model as-is because Salesforce AI is certified to be fair
Why it's wrong here
No AI model is automatically fair; fairness must be actively evaluated and ensured using domain expertise and auditing tools.
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