AI Associate Ethical Considerations of AI Practice Question
A company uses Einstein Prediction Builder to score leads. The model systematically gives lower scores to leads from a particular geographic region, even though those leads often convert. Which action should the company take to address this ethical concern?
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
✓
Retrain the model with a balanced dataset that includes more leads from the under-scored region.
Retraining with a balanced dataset helps mitigate bias by ensuring the model learns from a representative sample, aligning with fairness principles.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to a different AI vendor.
Why it's wrong here
This does not fix the underlying data or model issue and may not resolve bias.
- ✗
Ignore the bias because the model is proprietary.
Why it's wrong here
Ignoring bias violates ethical principles of fairness and accountability.
- ✓
Retrain the model with a balanced dataset that includes more leads from the under-scored region.
Why this is correct
Retraining with balanced data directly addresses the bias by giving equal representation.
- ✗
Remove the region field from the model entirely.
Why it's wrong here
Removing the field may hide bias but other correlated features could perpetuate it.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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