AI0-001 AI Security, Ethics and Governance Practice Question
A large hospital system deploys an AI triage system for emergency rooms. The system uses patient vitals and symptoms to recommend treatment priority. Six months after deployment, complaints arise that the system frequently underestimates the severity of symptoms for patients from certain ethnic backgrounds. A data scientist runs a bias audit and finds that the model's false negative rate is 20% higher for the minority group. The hospital's AI governance board requires immediate corrective action. The data science team has limited resources and cannot retrain the entire model from scratch. They have access to the training data, which is imbalanced. The model is a gradient boosted tree. Which course of action best addresses the bias while minimizing operational impact?
⚠ Common exam trap
CompTIA often tests the misconception that bias mitigation always requires retraining or complex algorithmic changes, when in fact post-processing threshold adjustments can be a quick, effective fix for deployed models with limited resources.
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
✓
Post-process the model's predictions by adjusting thresholds for the minority group
Post-processing by adjusting decision thresholds for the minority group directly compensates for the higher false negative rate without requiring retraining. Since the team has limited resources and cannot retrain the entire gradient boosted tree model, this approach minimizes operational impact while addressing the bias. The threshold adjustment effectively lowers the probability cutoff for the minority group, making the model more sensitive to their symptoms and reducing underestimation of severity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Rebalance the training data using SMOTE and retrain the model
Why it's wrong here
SMOTE synthesises minority-class examples, but the bias stems from disparate false negative rates across ethnic groups, not class imbalance alone; retraining also exceeds the stated resource limit. It is tempting because imbalance is present, and SMOTE would be correct if underrepresentation of a class were the sole cause.
- ✗
Use adversarial debiasing during training to remove protected attribute correlations
Why it's wrong here
Adversarial debiasing requires retraining the gradient boosted tree with a modified objective, which the team cannot do given limited resources. It is tempting because it directly targets protected-attribute correlations, and would be the right choice if full retraining from scratch were feasible and the goal were in-processing fairness.
- ✓
Post-process the model's predictions by adjusting thresholds for the minority group
Why this is correct
Threshold adjustment is a post-processing intervention applied at inference, so it corrects the disparate false negative rate without retraining the gradient boosted tree. This satisfies the constraint of limited resources and minimal operational impact, equalising error rates across groups while leaving the existing model intact.
- ✗
Replace the model with a simpler logistic regression model to improve interpretability
Why it's wrong here
Swapping to logistic regression discards the trained gradient boosted tree and does not guarantee reduced false negative disparity for the minority group. It is tempting because interpretability aids auditing, and would suit a scenario where explainability, not measured bias correction, is the governance board's stated requirement.
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.