Interpreting Fairness Metrics: Disparate Impact, Equal Opportunity, Demographic Parity
Exhibit
Refer to the exhibit.
```
{
"modelVersion": "1.0",
"features": ["age", "income", "credit_score", "zip_code"],
"fairnessEvaluation": {
"disparateImpact": 0.85,
"equalOpportunityDiff": 0.12,
"demographicParityDiff": 0.18
},
"thresholds": {
"disparateImpactMin": 0.8,
"equalOpportunityDiffMax": 0.1,
"demographicParityDiffMax": 0.1
}
}
```Refer to the exhibit. The fairness evaluation shows a disparate impact of 0.85, equal opportunity difference of 0.12, and demographic parity difference of 0.18. Which fairness thresholds are violated?
Quick Answer
The correct answer is that both equal opportunity and demographic parity thresholds are violated. This is because the equal opportunity difference of 0.12 exceeds the standard 0.1 threshold, meaning the model’s true positive rates differ unacceptably across groups, while the demographic parity difference of 0.18 also surpasses 0.1, indicating a significant imbalance in selection rates. The disparate impact of 0.85, however, falls within the acceptable range of 0.8 to 1.25, so it is not violated. On the Salesforce AI Associate exam, this question tests your ability to interpret fairness metrics and apply common industry thresholds, often appearing as a scenario where you must distinguish between acceptable and violated metrics. A common trap is assuming any value outside 1.0 is a violation, but disparate impact uses a wider band. To remember: think of equal opportunity and demographic parity as strict 0.1 gates, while disparate impact has a wider 0.8–1.25 corridor.
⚠ Common exam trap
Salesforce often tests the misconception that disparate impact is the only fairness metric that matters, but the trap here is that multiple fairness thresholds can be violated simultaneously, and candidates must check each metric against its specific threshold rather than assuming a single violation.
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
✓
Equal opportunity and demographic parity.
The fairness evaluation shows violations of both equal opportunity and demographic parity thresholds. The equal opportunity difference of 0.12 exceeds the commonly accepted threshold of 0.1, and the demographic parity difference of 0.18 exceeds the typical threshold of 0.1. Disparate impact of 0.85 is within the acceptable range (typically 0.8 to 1.25), so it is not violated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Demographic parity only.
Why it's wrong here
Equal opportunity is also violated.
- ✗
Equal opportunity only.
Why it's wrong here
Demographic parity is also violated.
- ✓
Equal opportunity and demographic parity.
Why this is correct
Both exceed their thresholds.
- ✗
Disparate impact only.
Why it's wrong here
Disparate impact is 0.85 >= 0.8, so it passes.
Quick reference
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Same concept, more angles
1 more way this is tested on AI Associate
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Refer to the exhibit. What does the "Status: FAIL" indicate?
medium- A.The model's overall accuracy is low.
- B.The data is missing age_group information.
- C.The model is not allowed for deployment.
- ✓ D.The model shows a significant disparity in true positive rates across age groups.
Why D: The exhibit shows an equal opportunity difference metric that measures the disparity in true positive rates across age groups. A value exceeding the threshold results in a Status: FAIL, indicating a significant disparity. Therefore, Option D is correct. Option A is incorrect because the failure is not about overall accuracy but about fairness. Option B is incorrect: the data clearly includes age_group information. Option C is incorrect because the failure does not directly indicate a deployment restriction; rather, it indicates a fairness issue that may need to be addressed before deployment.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI Associate practice question is part of Courseiva's free Salesforce certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI Associate exam.