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AI Associate Ethical Considerations of AI Practice Question

Exhibit

{
  "model_name": "LeadScoring_v2",
  "features": ["lead_source", "company_size", "industry", "email_engagement"],
  "fairness_metrics": {
    "demographic_parity": 0.85,
    "equal_opportunity": 0.72
  },
  "bias_threshold": 0.8,
  "current_performance": {"accuracy": 0.91, "f1_score": 0.88}
}

Refer to the exhibit. A Salesforce admin is reviewing an AI model's fairness report. Which action should the admin take?

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 because the equal opportunity score is below threshold.

The equal opportunity score (0.72) is below the bias threshold (0.8), indicating potential unfairness in true positive rates across groups. Option A is wrong because demographic parity is above threshold but equal opportunity is not, so not all metrics exceed threshold. Option C is wrong because removing features may not address the root cause. Option D is wrong because increasing the threshold would mask the problem.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Remove the email_engagement feature to improve fairness.
Why it's wrong here
Removing features may not fix the imbalance.
✓
Retrain the model because the equal opportunity score is below threshold.
Why this is correct
The low equal opportunity score indicates bias that needs mitigation.
✗
Increase the bias threshold to 0.9.
Why it's wrong here
Raising the threshold would hide the issue.
✗
Deploy the model because all metrics exceed the threshold.
Why it's wrong here
Equal opportunity is below the threshold.

Quick reference

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Go deeper

AI Associate Ethical Considerations of AI Practice Question

Exhibit

Why each option matters

Option-by-option breakdown

RAID Level Comparison

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