AI0-001 AI Security, Ethics and Governance Practice Question
An AI system used for resume screening is found to consistently rank male candidates higher than female candidates with similar qualifications. The HR director wants to remediate this bias without significantly reducing model accuracy. Which technique should be applied?
⚠ Common exam trap
CompTIA often tests the misconception that simply removing the protected attribute (e.g., gender) from the dataset is sufficient to eliminate bias, but candidates must understand that bias can persist through correlated features (proxy discrimination).
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 to the model during training.
Adversarial debiasing is the correct technique because it directly addresses bias during training by introducing an adversarial network that attempts to predict the protected attribute (e.g., gender) from the model's predictions. The main model is trained to maximize accuracy while minimizing the adversary's ability to infer the protected attribute, thereby reducing bias without a significant drop in predictive performance. This approach is more effective than simple feature removal or data collection because it actively learns to remove correlations between the protected attribute and the output.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Apply adversarial debiasing to the model during training.
Why this is correct
Adversarial debiasing trains a classifier to predict the protected attribute while the main model learns to prevent it, removing gender-correlated signals from the representation. This reduces disparate ranking while preserving predictive performance, meeting the HR director's accuracy constraint.
- ✗
Use a random selection of candidates to avoid bias.
Why it's wrong here
Random selection discards the model's predictive signal entirely, so accuracy collapses and qualified candidates are ignored. Randomisation suits exploratory sampling or auditing, not remediation of a ranking model that must retain its accuracy while removing the gender-linked disparity.
- ✗
Remove the gender feature from the dataset and retrain.
Why it's wrong here
Dropping gender leaves correlated proxies such as names, universities or hobbies that still encode the bias, so disparity can remain. Feature removal suits simple compliance-driven exclusion, whereas the stem requires correcting an observed ranking disparity without materially degrading accuracy, which needs bias mitigation.
- ✗
Collect more training data from underrepresented groups.
Why it's wrong here
Adding data from underrepresented groups can improve representation but does not remove the label bias already encoded in historical hiring decisions, so rankings may persist. It suits improving overall coverage or rare-case performance, not directly correcting a measured disparity between similarly qualified candidates.
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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.