AAIR AI Risk Program Management Practice Question
When setting KPIs for an AI risk program, which metric is a leading indicator of potential model bias?
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
✓
Demographic parity ratio in training data samples
Monitoring training data distribution is a leading indicator, whereas output monitoring is a lagging indicator.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Average model inference latency
Why it's wrong here
Latency is a performance metric, not a bias metric.
- ✗
Percentage of customer complaints regarding AI decisions
Why it's wrong here
This is a lagging indicator of output issues.
- ✗
Number of model updates per quarter
Why it's wrong here
Update frequency is an operational metric.
- ✓
Demographic parity ratio in training data samples
Why this is correct
Analyzing training data for representativeness identifies bias before the model is deployed.
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.