Quick Answer
The answer is demographic parity in predictions, as it directly measures whether an AI model’s outcomes are independent of sensitive attributes like race or gender. This factor is quantified using the disparate impact ratio, which compares selection rates across demographic groups—a ratio below 0.8 or above 1.25 typically signals adverse impact and unfair treatment. On the Salesforce AI Associate exam, fairness evaluation metrics test your ability to identify quantitative checks for bias, often appearing in scenario-based questions where you must choose which metrics detect disproportionate harm. A common trap is confusing demographic parity with equal opportunity, which focuses on true positive rates instead of overall selection rates. Remember the 0.8 rule: if the ratio dips below that threshold, the model likely fails fairness checks.
This AI Associate practice question tests your understanding of ethical considerations of ai. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which THREE factors should be considered when evaluating the fairness of an AI model?
Disparate impact ratio across groups.
Measures adverse impact ratio.
Overall accuracy on the test set.
Why wrong: Accuracy does not reflect group fairness.
Model training time.
Why wrong: Training time is unrelated to fairness.
Equal opportunity difference.
Equal opportunity ensures similar true positive rates.
Demographic parity in predictions.
Demographic parity requires equal prediction rates across groups.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Disparate impact ratio across groups.
Option A is correct because the disparate impact ratio measures whether an AI model's predictions disproportionately harm or benefit certain demographic groups, typically by comparing selection rates across groups. A ratio below 0.8 or above 1.25 is often considered evidence of adverse impact, making it a key quantitative fairness metric.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Disparate impact ratio across groups.
Why this is correct
Measures adverse impact ratio.
Related concept
Read the scenario before looking for a memorised answer.
Overall accuracy on the test set.
Why it's wrong here
Accuracy does not reflect group fairness.
Model training time.
Why it's wrong here
Training time is unrelated to fairness.
Equal opportunity difference.
Why this is correct
Equal opportunity ensures similar true positive rates.
Related concept
Read the scenario before looking for a memorised answer.
Demographic parity in predictions.
Why this is correct
Demographic parity requires equal prediction rates across groups.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Salesforce often tests the distinction between performance metrics (like accuracy) and fairness metrics, trapping candidates who assume a high-accuracy model is automatically fair.
Detailed technical explanation
Fairness metrics like disparate impact ratio, equal opportunity difference, and demographic parity each capture different normative definitions of fairness. For example, equal opportunity difference checks whether the true positive rate is equal across groups, while demographic parity requires that the proportion of positive predictions is the same for all groups. In practice, these metrics often conflict, so selecting which to use depends on the specific ethical and legal context of the AI application.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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What to study next
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FAQ
Ethical Considerations of AI — This question tests Ethical Considerations of AI — Read the scenario before looking for a memorised answer..
The correct answer is: Disparate impact ratio across groups. — Option A is correct because the disparate impact ratio measures whether an AI model's predictions disproportionately harm or benefit certain demographic groups, typically by comparing selection rates across groups. A ratio below 0.8 or above 1.25 is often considered evidence of adverse impact, making it a key quantitative fairness metric.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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