AI0-001 AI Concepts and Foundations Practice Question
A financial institution uses a machine learning model to approve loan applications. The model was trained on historical data that inadvertently encoded a bias against applicants from certain zip codes, leading to discriminatory lending practices. A recent audit reveals that the model's decisions are unfair, and regulators require the bank to remediate the bias without significantly reducing overall approval accuracy. The data science team has access to the training data, the model, and a set of fairness metrics. They also have a small, unbiased validation set. Which course of action should the team take to satisfy regulatory requirements?
⚠ Common exam trap
CompTIA often tests the misconception that removing a sensitive feature (like zip code) is sufficient to eliminate bias, but the trap is that models can learn proxy features, so a more sophisticated debiasing technique like adversarial debiasing is required.
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
✓
Implement adversarial debiasing using the unbiased validation set to enforce fairness constraints
Adversarial debiasing directly addresses the bias encoded in the model by training a predictor and an adversary simultaneously. The adversary tries to predict the protected attribute (e.g., zip code) from the model's predictions, while the predictor is penalized for allowing such inference, enforcing fairness constraints. Using the unbiased validation set ensures the debiasing process is guided by ground truth labels that are free from historical bias, allowing the model to retain high accuracy while reducing discrimination.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove the zip code feature from the model and retrain
Why it's wrong here
Bias may persist through correlated features like income or credit history.
- ✓
Implement adversarial debiasing using the unbiased validation set to enforce fairness constraints
Why this is correct
Adversarial debiasing directly optimizes for fairness and accuracy.
- ✗
Increase the weight of samples from disadvantaged zip codes in the training data
Why it's wrong here
Reweighting may not achieve strict fairness and can distort the model.
- ✗
Retrain the model using only the unbiased validation set
Why it's wrong here
The validation set is too small for training and may not represent the population.
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.