hardMultiple ChoiceObjective-mapped
AIF-C01 Practice Question: Using a machine learning model to predict…
A company is using a machine learning model to predict employee turnover. The model's predictions are used to identify at-risk employees for retention efforts. The company wants to ensure that the model does not inadvertently discriminate against employees based on age. Which metric should be used to measure fairness across age groups?
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
✓
Equalized odds
Equalized odds requires that the model's true positive rate and false positive rate are equal across groups, which is appropriate for binary outcomes like turnover prediction.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Equalized odds
Why this is correct
Equalized odds ensures equal true positive and false positive rates across groups.
- ✗
SHAP feature importance
Why it's wrong here
SHAP is for explainability, not fairness measurement.
- ✗
Disparate impact ratio
Why it's wrong here
Disparate impact ratio is often used for binary decisions but does not consider error rates.
- ✗
Demographic parity
Why it's wrong here
Demographic parity only checks the proportion of positive outcomes, not error rates.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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