AAIR AI Risk Governance And Framework Integration Practice Question
Which THREE factors should be evaluated when reviewing an AI model for 'Fairness'?
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
✓
Performance disparity across different demographic cohorts.
Fairness assessment covers data representation, metric definitions, and outcome disparities.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Performance disparity across different demographic cohorts.
Why this is correct
Fairness is measured by comparing outcomes between groups.
- ✗
The number of hours the server was powered on.
Why it's wrong here
Server hours are not related to fairness.
- ✓
Defined metrics for success that explicitly account for equity.
Why this is correct
Defining fairness metrics is the starting point for evaluation.
- ✓
Training data representation across protected groups.
Why this is correct
Under-representation is a primary cause of bias.
- ✗
The model's file size in kilobytes.
Why it's wrong here
File size is irrelevant to fairness.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official ISACA exam blueprint
This AAIR practice question is part of Courseiva's free ISACA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AAIR exam.