AAIR AI Lifecycle Risk Management Practice Question
Why is 'Explainability' considered a key risk management control in the AI lifecycle?
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
✓
It enables identification of potential bias, errors, or unexpected behavior in model decision-making.
Explainability allows stakeholders to audit decisions and ensure they comply with regulatory requirements (like GDPR).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It guarantees the model will never fail.
Why it's wrong here
No control can guarantee zero failure rates.
- ✗
It increases the inference speed of the model.
Why it's wrong here
Explainability often adds overhead to inference.
- ✓
It enables identification of potential bias, errors, or unexpected behavior in model decision-making.
Why this is correct
Understanding *why* a model made a decision is essential for debugging and legal compliance.
- ✗
It allows the model to train on less data.
Why it's wrong here
Explainability does not reduce the data requirements for training.
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.