AAIA AI Governance And Risk Practice Question
A data scientist proposes using a 'black-box' model for a high-stakes loan approval process. Which action does your AI audit framework require?
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
✓
Mandating the use of XAI (Explainable AI) tools to interpret model decisions
Explainability is a prerequisite for models that have significant impact on individuals, as per most AI governance frameworks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Mandating the use of XAI (Explainable AI) tools to interpret model decisions
Why this is correct
For high-stakes decisions, auditability and transparency require that decisions be explainable.
- ✗
Requiring the data scientist to publish the code on an internal repository
Why it's wrong here
Publishing code does not provide the logic behind 'black-box' decisions.
- ✗
Allowing the model if it achieves 99% accuracy on the test set
Why it's wrong here
Accuracy does not compensate for a lack of transparency in high-impact decisions.
- ✗
Asking the marketing team to verify the model's fairness
Why it's wrong here
Marketing is not qualified to assess model fairness; that is a risk/compliance function.
About these practice questions
This AAIA question is part of Courseiva's 209-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. 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 AAIA 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 AAIA exam.