AI0-001 AI Security, Ethics and Governance Practice Question
An organization uses an AI-based hiring tool. To prevent bias, they want to ensure the model's decisions are explainable. Which approach is most suitable?
⚠ Common exam trap
The AI0-001 exam often tests the distinction between a model that is inherently interpretable (like logistic regression) versus a model that is explained post-hoc (like SHAP on a deep network), where the trap is that candidates assume any explanation method makes a black-box model 'explainable' in the same way.
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
✓
Use a simpler interpretable model like logistic regression
A simpler interpretable model like logistic regression provides inherent transparency—its coefficients directly show the weight and direction of each input feature on the hiring decision. This makes it easy for auditors and stakeholders to verify that the model is not using protected attributes (e.g., race, gender) in a biased way, without needing post-hoc explanation tools. The key is that the model itself is interpretable by design, not just explained after the fact.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use reinforcement learning with fairness constraints
Why it's wrong here
Fairness constraints shape reward and policy outcomes but do not expose why a specific hiring decision was reached, so the model remains opaque to auditors and candidates. It is tempting because reinforcement learning with constraints can reduce measured disparity, and it would fit a scenario optimising sequential decisions rather than explaining individual classifications.
- ✓
Use a simpler interpretable model like logistic regression
Why this is correct
Logistic regression produces inherently interpretable coefficients, so each hiring decision can be traced to specific feature weights — directly satisfying the explainability requirement. Unlike black-box models needing post-hoc tools such as SHAP or LIME, its reasoning is transparent by design, making bias auditing straightforward.
- ✗
Use a black-box deep learning model with SHAP explanations
Why it's wrong here
SHAP values approximate a black-box model's behaviour after training, so explanations are post-hoc and cannot guarantee the decision logic itself is unbiased or auditable. It is tempting because SHAP is a recognised explainability tool, and it would suit scenarios where predictive accuracy outweighs the need for inherently interpretable hiring decisions.
- ✗
Use ensemble methods with feature importance
Why it's wrong here
Feature importance from an ensemble describes aggregate contribution across the training set, not the reasoning behind an individual candidate's outcome, so per-decision justification is unavailable. It is tempting because ensembles often score well and importance plots look explanatory, and it would suit model-level diagnostics rather than candidate-level explainability.
About these practice questions
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.