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AI Security, Ethics and GovernancehardMultiple ChoiceObjective-mapped

AI0-001 AI Security, Ethics and Governance Practice Question

A financial institution uses an AI model to approve loan applications. The model was trained on historical data that included biased lending practices. The bank's ethics committee wants to mitigate bias without removing protected attributes. Which approach best balances fairness and model performance?

⚠ Common exam trap

CompTIA often tests the misconception that simply removing protected attributes (Option B) is sufficient to eliminate bias, when in reality proxy features and correlated variables can perpetuate 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 during training

Adversarial debiasing is the best approach because it directly optimizes the model to reduce bias during training while preserving predictive accuracy. It uses an adversarial network that tries to predict the protected attribute from the model's predictions, forcing the main model to learn representations that are less correlated with that attribute. This allows the bank to keep protected attributes in the data (as required by the ethics committee) while actively mitigating bias.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Retrain the model using a balanced dataset

    Why it's wrong here

    Balancing data improves representation but does not eliminate bias from feature interactions.

  • Remove all protected attributes from the training data

    Why it's wrong here

    Bias can persist through correlated features, so removal alone is insufficient.

  • Post-process model outputs to adjust for demographic parity

    Why it's wrong here

    Post-processing can hurt performance and may not address root cause.

  • Apply adversarial debiasing during training

    Why this is correct

    Adversarial debiasing reduces bias by learning non-discriminatory representations.

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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.