AI0-001 AI Security, Ethics and Governance Practice Question
A government agency uses an AI system to prioritize emergency response calls. An auditor finds that the model's decisions cannot be explained to citizens. Which governance mechanism is most appropriate to address this?
⚠ Common exam trap
The trap here is assuming that explainability requires sacrificing model performance or that publishing data equals explaining decisions.
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
✓
Implement a right-to-explanation process with model-agnostic explanation tools.
A right-to-explanation process with model-agnostic tools directly provides understandable reasons for automated decisions, which is the governance mechanism the auditor's finding requires. Replacing the model, publishing data, or disclaiming review do not satisfy the need for contestable, explainable decisions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Publish the model's full training dataset.
Why it's wrong here
Publishing training data raises serious privacy and security concerns and does not explain individual decisions. It may violate data protection laws and expose sensitive information. Transparency about data is different from explaining a specific automated decision, so this does not address the auditor's finding.
- ✗
Replace the model with a simpler linear regression.
Why it's wrong here
A simpler model may be more interpretable, but replacing a complex emergency-response system can reduce accuracy and is not required by governance principles. The issue is explainability to citizens, not model architecture. A right-to-explanation process can be added without sacrificing performance, and a linear model may still be difficult for laypeople to understand.
- ✗
Add a disclaimer that decisions are final and not subject to review.
Why it's wrong here
A disclaimer that removes review contradicts due process and ethical AI governance. It would worsen the accountability gap and likely violate legal requirements for contestability. The appropriate response is to enable explanation and appeal, not to deny them.
- ✓
Implement a right-to-explanation process with model-agnostic explanation tools.
Why this is correct
A right-to-explanation process gives affected individuals understandable reasons for automated decisions, satisfying due process and ethical governance. Model-agnostic tools such as LIME or SHAP can approximate feature contributions even for complex models. This directly addresses the auditor's finding by making decisions contestable and transparent without requiring a full model rebuild.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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