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Ethical Considerations of AImediumMultiple ChoiceObjective-mapped

AI Associate Ethical Considerations of AI Practice Question

Exhibit

{
  "fairness_checks": {
    "demographic_parity": true,
    "equal_opportunity": false,
    "disparate_impact": true
  },
  "threshold": 0.8
}

Refer to the exhibit. Based on the JSON policy for AI fairness checks, which fairness metric is NOT enabled?

⚠ Common exam trap

Salesforce often tests the distinction between equalized odds and equal opportunity, trapping candidates who assume equalized odds automatically includes equal opportunity, when in fact they are separate metrics with different mathematical definitions.

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

(Equal opportunity) is correct because the JSON policy shown in the exhibit configures fairness checks for demographic parity, disparate impact, and equalized odds, but does NOT include the equal opportunity metric. Equal opportunity requires equal true positive rates across groups, which is a separate metric from equalized odds and must be explicitly enabled in the policy definition.

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

    Why it's wrong here

    It is set to true.

  • All are enabled

    Why it's wrong here

    Equal opportunity is not enabled.

  • Disparate impact

    Why it's wrong here

    It is set to true.

  • Equal opportunity

    Why this is correct

    Correct. The 'equal_opportunity' field is false.

Quick reference

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