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Ethical Considerations of AIeasyMultiple ChoiceObjective-mapped

AI Associate Ethical Considerations of AI Practice Question

A company is designing an AI system to screen job applicants. To ensure fairness, which practice should be implemented?

⚠ Common exam trap

Salesforce often tests the misconception that removing demographic data (option D) is sufficient to ensure fairness, when in reality proxy variables and model behavior must be actively monitored through audits.

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

Conduct regular fairness audits on model outcomes

Regular fairness audits are essential because they systematically evaluate model outcomes for bias across demographic groups, using metrics like disparate impact or equal opportunity difference. This practice aligns with responsible AI frameworks (e.g., NIST AI Risk Management Framework) and helps detect subtle biases that may emerge from proxy variables or data drift, ensuring the screening process remains equitable over time.

Answer analysis

Option-by-option breakdown

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

  • Use only one data source for consistency

    Why it's wrong here

    Relying on one source can embed bias; diverse data is better.

  • Maximize the model's accuracy on historical hiring decisions

    Why it's wrong here

    Historical decisions may be biased, so maximizing accuracy perpetuates bias.

  • Conduct regular fairness audits on model outcomes

    Why this is correct

    Audits help detect and address disparate impact.

  • Remove all demographic data from the training set

    Why it's wrong here

    Removing demographic data does not remove bias; proxies may remain.

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