AAIR AI Risk Program Management Practice Question
Which TWO strategies are recommended for 'Mitigating' AI 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
✓
Applying fairness constraints during model training
Mitigation involves both pre-processing (data) and in-processing (training) techniques.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Removing all data from the database
Why it's wrong here
This renders the AI useless.
- ✗
Asking the user to manually fix the bias
Why it's wrong here
This shifts risk to the user, which is inappropriate.
- ✓
Applying fairness constraints during model training
Why this is correct
Fairness algorithms directly mitigate bias during the build phase.
- ✗
Ignoring the bias and hoping it goes away
Why it's wrong here
This is not a strategy; it is negligence.
- ✓
Training on more diverse and representative datasets
Why this is correct
Addressing bias at the data source is the most effective mitigation.
About these practice questions
One of 199 original AAIR practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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.