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
Aggregate accuracy can remain high while a subgroup is systematically misdiagnosed, which is precisely the harm in clinical deployment. Disparity must be measured and mitigated per group during training. Ignoring it suits only non-consequential, evenly distributed error tolerances.
- ✗
Retrain the model with more data from the underperforming group
Why it's wrong here
Adding data from the underperforming group improves representation but does not by itself correct the model's learned decision boundary, so disparity can persist. Dedicated bias mitigation such as reweighting or equalised odds adjusts training objectives directly. More data suits general accuracy or coverage gaps.
- ✓
Apply adversarial debiasing during training
Why this is correct
Adversarial debiasing trains a predictor alongside an adversary that tries to infer the protected attribute from its outputs, forcing representations that cannot distinguish demographic groups. This directly reduces the accuracy gap during training, satisfying the stem's requirement to cut bias between groups.
- ✗
Remove demographic attributes from the training data
Why it's wrong here
Removing demographic attributes does not remove bias, because correlated proxies such as postcode or prior utilisation still encode group membership. Bias mitigation techniques such as reweighting or adversarial debiasing act on training directly. Attribute removal suits privacy compliance, not fairness correction.
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.