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Ethical Considerations of AImediumMultiple ChoiceObjective-mapped

AI Associate Ethical Considerations of AI Practice Question

A company's Einstein Sentiment model is used to flag negative customer feedback. The model was trained on English reviews only. When deployed globally, it misclassifies positive reviews in Spanish as negative. What is the primary ethical concern?

⚠ Common exam trap

Salesforce often tests the distinction between a model's technical flaw (low accuracy) and the ethical principle it violates (fairness), tricking candidates into picking the symptom over the root 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

The model is unfair to Spanish-speaking customers.

The primary ethical concern is fairness: the model was trained exclusively on English reviews, so it systematically misclassifies Spanish positive feedback as negative. This creates a disparate impact on Spanish-speaking customers, violating the principle of algorithmic fairness. The issue is not just low accuracy but an unjust bias that disadvantages a specific linguistic group.

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 is not interpretable.

    Why it's wrong here

    Interpretability is separate from fairness.

  • The model has low accuracy for Spanish reviews.

    Why it's wrong here

    Low accuracy is a symptom, but the core is fairness.

  • The model is unfair to Spanish-speaking customers.

    Why this is correct

    Lack of representation leads to unfair treatment.

  • The model violates privacy regulations.

    Why it's wrong here

    No privacy issue mentioned.

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Written by Johnson Ajibi, MSc IT Security

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