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Ethical Considerations of AIhardMultiple ChoiceObjective-mapped

AI Associate Ethical Considerations of AI Practice Question

Exhibit

Refer to the exhibit.

```json
{
  "modelName": "LeadScoring_v2",
  "features": ["LeadSource", "Industry", "CompanySize", "EmailDomain", "NumberOfEmployees"],
  "target": "Converted",
  "trainingData": {
    "source": "Salesforce_Leads_2019-2021",
    "recordCount": 50000,
    "classBalance": {"Converted": 5000, "NotConverted": 45000}
  },
  "evaluationMetrics": {
    "accuracy": 0.92,
    "precision": 0.85,
    "recall": 0.30
  }
}
```

An AI Associate reviews the Lead Scoring model exhibit. What is the primary ethical concern with this model?

⚠ Common exam trap

Salesforce often tests the distinction between a technical problem (like imbalanced data) and its ethical consequence (like low recall causing unfair outcomes), so candidates mistakenly pick the technical cause (D) instead of the ethical impact (B).

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

The model has low recall, potentially missing minority class leads.

The primary ethical concern is that the model has low recall, meaning it fails to identify a significant portion of actual positive leads (the minority class). In a lead scoring context, this can result in missed business opportunities and potential bias against certain customer segments, as the model systematically overlooks valuable leads that do not fit the majority pattern.

Answer analysis

Option-by-option breakdown

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

  • The model uses too many features.

    Why it's wrong here

    Number of features is not an ethical concern by itself.

  • The model has low recall, potentially missing minority class leads.

    Why this is correct

    Low recall can lead to underrepresentation of certain groups.

  • The model is not explainable.

    Why it's wrong here

    The model type is not specified; interpretability is not directly addressed.

  • The training data is imbalanced.

    Why it's wrong here

    Imbalance is a technical concern, but the ethical concern is the impact of low recall.

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