- A
Fairness metrics such as equal opportunity difference
Quantifies specific fairness criteria.
- B
Feature importance scores
Why wrong: High importance does not directly indicate bias.
- C
Confusion matrix on the entire dataset
Why wrong: Without subgroup breakdown, bias may be hidden.
- D
Cross-validation accuracy
Why wrong: Accuracy alone does not indicate bias.
- E
Disparate impact analysis
Measures if outcomes disproportionately affect protected groups.
Question 356 of 1,000
AI Bias Detection Techniques
This AI0-001 practice question tests your understanding of ai security, ethics and governance. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which TWO of the following are effective techniques for detecting bias in an AI model?
Quick Answer
The answer is disparate impact analysis and fairness metrics. Disparate impact analysis is effective because it directly measures whether an AI model produces systematically different outcomes across protected groups, such as race or gender, by calculating ratios like the four-fifths rule. Fairness metrics, such as equal opportunity or demographic parity, quantify bias by comparing error rates or positive prediction rates between groups, providing a clear statistical benchmark. On the CompTIA AI+ AI0-001 exam, this question tests your ability to distinguish bias detection from general model evaluation—a common trap is confusing cross-validation or feature importance with fairness checks. Remember that bias detection focuses on outcome disparities, not model accuracy. A useful memory tip: think “disparate impact digs into group differences, while fairness metrics measure the gap.”
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco 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.
Detailed technical explanation
How to think about this question
Equal opportunity difference is part of the broader family of group fairness metrics defined in the 'Fairness Definitions Explained' (Verma & Rubin, 2018). It is computed as the difference in true positive rates between groups, and a common threshold for acceptable bias is 0.1 or less. Disparate impact analysis, originating from US employment law (the 80% rule), compares selection rates across groups using a ratio; a value below 0.8 indicates adverse impact. Both techniques require access to protected attribute labels in the test data.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A junior network technician can log in to a core router but cannot reach the enable prompt or configuration mode. The AAA server is authenticating the login — but the authorisation policy only grants privilege level 1, not 15. Authentication (who you are) is working; authorisation (what you can do) is not.
What to study next
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Security, Ethics and Governance — This question tests AI Security, Ethics and Governance — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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