- A
Data privacy in AI training
Why wrong: Data privacy is about protecting training data, not decision explainability.
- B
Vendor lock-in
Why wrong: Vendor lock-in is a procurement risk, not AI-specific.
- C
Adversarial attacks
Why wrong: Adversarial attacks manipulate inputs, not directly addressed by explainability.
- D
Model bias
Explainability helps identify and mitigate bias in AI decisions.
Question 199 of 1,000
CRISC Information Technology and Security Practice Question
This CRISC practice question tests your understanding of information technology and security. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
An organization uses AI/ML for credit scoring decisions. The risk manager is concerned about regulatory compliance if the model cannot explain its decisions. Which AI risk is most directly addressed by requiring explainability?
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
✓
Model bias
Requiring explainability in an AI/ML credit scoring model directly addresses model bias because it forces the model to reveal which input features (e.g., income, zip code) drive its decisions. Without explainability, the organization cannot detect or prove that the model is not discriminating against protected groups, violating regulations like the Equal Credit Opportunity Act (ECOA) or GDPR's right to explanation. Explainability is the primary technical control to audit and mitigate bias in automated decision-making.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Data privacy in AI training
Why it's wrong here
Data privacy is about protecting training data, not decision explainability.
- ✗
Vendor lock-in
Why it's wrong here
Vendor lock-in is a procurement risk, not AI-specific.
- ✗
Adversarial attacks
Why it's wrong here
Adversarial attacks manipulate inputs, not directly addressed by explainability.
- ✓
Model bias
Why this is correct
Explainability helps identify and mitigate bias in AI decisions.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'model bias' with 'data privacy' or 'adversarial attacks,' but the question specifically ties explainability to regulatory compliance, which is fundamentally about detecting and proving fairness (bias), not about data protection or input manipulation.
Detailed technical explanation
How to think about this question
Explainability techniques like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) compute feature importance scores to show how each input contributed to a specific prediction. In credit scoring, a black-box model might deny a loan based on a correlated proxy for race (e.g., zip code), and without explainability, the bias remains hidden. Real-world regulators, such as the CFPB, have explicitly required lenders to provide adverse action notices that explain the key factors for denial, making explainability a non-negotiable compliance requirement.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the CRISC exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Information Technology and Security — study guide chapter
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FAQ
Questions learners often ask
What does this CRISC question test?
Information Technology and Security — This question tests Information Technology and Security — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Model bias — Requiring explainability in an AI/ML credit scoring model directly addresses model bias because it forces the model to reveal which input features (e.g., income, zip code) drive its decisions. Without explainability, the organization cannot detect or prove that the model is not discriminating against protected groups, violating regulations like the Equal Credit Opportunity Act (ECOA) or GDPR's right to explanation. Explainability is the primary technical control to audit and mitigate bias in automated decision-making.
What should I do if I get this CRISC question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This CRISC 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 CRISC exam.
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