AI0-001 AI Security, Ethics and Governance Practice Question
A healthcare AI system used for diagnosis shows a significant accuracy difference between demographic groups. Which technique should be applied to directly reduce this bias during model training?
⚠ Common exam trap
The AI0-001 exam often tests the misconception that 'fairness through unawareness' (removing demographic attributes) is sufficient to eliminate bias, but the trap here is that proxy variables and correlated features can still cause disparate impact, making adversarial debiasing a more robust in-processing technique.
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
✓
Apply adversarial debiasing during training
Adversarial debiasing directly reduces bias during model training by introducing an adversarial network that attempts to predict the protected attribute (e.g., demographic group) from the model's predictions. The primary model is trained to maximize accuracy while simultaneously minimizing the adversary's ability to infer the protected attribute, thereby forcing the model to learn representations that are invariant to that attribute. This technique directly addresses the accuracy disparity by encoding fairness as an optimization objective, unlike post-hoc or data-level approaches.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Ignore the disparity as long as overall accuracy is acceptable
Why it's wrong here
Ignoring bias violates ethical guidelines and regulatory requirements.
- ✗
Retrain the model with more data from the underperforming group
Why it's wrong here
While adding data may help, it does not directly remove bias and may be impractical.
- ✓
Apply adversarial debiasing during training
Why this is correct
Adversarial debiasing explicitly penalizes the model for encoding demographic information, reducing bias.
- ✗
Remove demographic attributes from the training data
Why it's wrong here
Removing attributes often fails to eliminate bias because correlated features can proxy for demographics.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 754 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.