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AI Security, Ethics and GovernancemediumMultiple SelectObjective-mapped

AI0-001 AI Security, Ethics and Governance Practice Question

Which TWO of the following are effective techniques for detecting bias in an AI model?

⚠ Common exam trap

The AI0-001 exam often tests the distinction between model performance metrics (accuracy, confusion matrix) and fairness-specific metrics, leading candidates to mistakenly select cross-validation accuracy or feature importance as bias detection tools.

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

Fairness metrics such as equal opportunity difference

Fairness metrics such as equal opportunity difference directly quantify bias by measuring the difference in true positive rates between privileged and unprivileged groups. A value of zero indicates perfect fairness, while non-zero values reveal disparate treatment, making it a standard technique for bias detection in AI models.

Answer analysis

Option-by-option breakdown

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

  • Fairness metrics such as equal opportunity difference

    Why this is correct

    Quantifies specific fairness criteria.

  • Feature importance scores

    Why it's wrong here

    High importance does not directly indicate bias.

  • Confusion matrix on the entire dataset

    Why it's wrong here

    Without subgroup breakdown, bias may be hidden.

  • Cross-validation accuracy

    Why it's wrong here

    Accuracy alone does not indicate bias.

  • Disparate impact analysis

    Why this is correct

    Measures if outcomes disproportionately affect protected groups.

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.