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
Option A, fairness metrics such as equal opportunity difference, is correct because it quantitatively compares true positive rates across protected groups, directly exposing unequal model performance that indicates bias. Option E, disparate impact analysis, is correct because it applies the four-fifths (80%) rule to compare selection or approval rates between groups, a standard legal and statistical method for detecting discriminatory outcomes. Options B, C, and D are not marked correct: feature importance scores only show which inputs influence predictions and do not by themselves reveal bias against a group; a confusion matrix on the entire dataset aggregates all groups and hides per-group disparities unless disaggregated; and cross-validation accuracy measures overall generalization performance, not fairness across subgroups.
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
Equal opportunity difference compares true positive rates across protected groups, revealing whether a model misses positive cases disproportionately for one group. This group-fairness metric directly quantifies disparate error rates, making it an effective bias detection technique.
- ✗
Feature importance scores
Why it's wrong here
Feature importance scores rank which inputs influence predictions globally; they do not measure outcome disparities between demographic groups. It is tempting because importance reveals which attributes drive the model, which suits feature selection and explainability, but bias detection requires comparing error or selection rates across groups.
- ✗
Confusion matrix on the entire dataset
Why it's wrong here
A confusion matrix on the entire dataset reports overall true and false rates, which aggregate across groups and conceal disparate error rates. It is tempting because confusion matrices expose classification errors, which suits evaluating overall model performance, but bias detection requires metrics disaggregated by subgroup.
- ✗
Cross-validation accuracy
Why it's wrong here
Cross-validation accuracy averages performance across folds, masking subgroup-specific error differences behind a single aggregate figure. It is tempting because cross-validation estimates generalisation reliably, which suits model selection and tuning, but bias detection needs per-group metrics rather than one pooled accuracy.
- ✓
Disparate impact analysis
Why this is correct
Disparate impact analysis quantifies whether a model's outcomes disadvantage a protected group, comparing selection rates across demographics to expose statistical disparity. This directly satisfies the stem's requirement for bias detection by measuring outcome fairness rather than relying on intuition, revealing discriminatory patterns that accuracy metrics alone would conceal.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 962 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.