AI0-001 AI Security, Ethics and Governance Practice Question
A city agency deploys an AI system that scores permit applications. The vendor refuses to disclose model weights or feature importance, citing trade secrets. The agency's oversight board must still meet its obligation to explain adverse decisions to applicants. Which approach best satisfies that obligation?
⚠ Common exam trap
The trap here is believing that meaningful explanation requires access to model weights, when model-agnostic techniques can produce per-decision reasons without disclosing proprietary internals.
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
✓
Require the vendor to supply model-agnostic explanations, such as local surrogate or counterfactual reason codes, for each adverse decision.
The agency must explain adverse decisions without forcing the vendor to reveal proprietary internals. Model-agnostic explainability, including local surrogates and counterfactual reason codes, derives per-decision explanations from observable behavior, so applicants receive meaningful factors and the board meets its duty. Full disclosure, generic notices, and replacing the system each either breach the constraint or fail to provide decision-specific reasoning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Require the vendor to supply model-agnostic explanations, such as local surrogate or counterfactual reason codes, for each adverse decision.
Why this is correct
Model-agnostic techniques generate explanations from input-output behavior without exposing proprietary weights, so the agency can give applicants concrete reasons while the vendor keeps its intellectual property. Local surrogates and counterfactual reason codes translate a decision into interpretable factors. This satisfies the oversight duty and preserves the commercial relationship, which is exactly the constraint the scenario describes.
- ✗
Inform applicants that the decision was made by an automated system and provide a generic appeal link.
Why it's wrong here
Telling an applicant that a machine decided, without any factor-level reason, does not meet a meaningful explanation duty and gives the person nothing to contest. A generic appeal link shifts the burden back to the applicant. The board's obligation is to convey why the decision went against the person, which requires decision-specific factors rather than a blanket automated-processing notice.
- ✗
Replace the vendor model with a transparent rule-based scoring system that the agency builds in-house.
Why it's wrong here
A rule-based rebuild may be more interpretable, but it discards the vendor's predictive performance, costs significant time and money, and is not necessary to meet the explanation duty. The scenario asks how to satisfy oversight while the vendor protects its secrets, not how to re-platform. Substitution is an overreaction that ignores the available explainability tooling.
- ✗
Publish the vendor's source code and trained weights so independent researchers can audit the decisions.
Why it's wrong here
Full disclosure would breach the vendor's trade-secret position and likely end the contract, and it is not required to give an applicant a reason for an adverse decision. Publishing weights also creates security exposure. The oversight board needs per-decision explainability, not wholesale release of proprietary artifacts, so this approach is disproportionate to the stated obligation.
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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
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.