AI0-001 AI Concepts and Foundations Practice Question
A company develops an AI model that recommends job candidates. The model inadvertently discriminates against a protected group. Which approach is most effective for mitigating this bias?
⚠ Common exam trap
CompTIA often tests the misconception that removing a protected attribute from training data is sufficient to eliminate bias, but the trap is that models can still discriminate through correlated proxy features, making fairness-aware algorithms necessary.
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
✓
Use a fairness-aware machine learning algorithm
Fairness-aware machine learning algorithms explicitly incorporate fairness constraints or objectives during model training, directly addressing and mitigating bias against protected groups. Unlike simple removal of protected attributes, these algorithms can detect and correct for proxy discrimination and disparate impact, ensuring the model's recommendations are equitable by design.
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 protected attribute from the training data
Why it's wrong here
Removing the protected attribute does not eliminate bias, because correlated proxy features still encode group membership and the model learns the same patterns. It is tempting as a quick fix, but fairness requires measuring outcomes across groups and correcting disparities, not merely hiding the attribute.
- ✓
Use a fairness-aware machine learning algorithm
Why this is correct
Fairness-aware algorithms incorporate bias constraints or regularisation directly into the training objective, reducing disparate impact at the model level rather than masking it post hoc. This addresses the discriminatory outcome against a protected group, which pre-processing or threshold tweaks alone cannot reliably fix.
- ✗
Analyze model predictions after deployment
Why it's wrong here
Analysing predictions after deployment detects bias but does not mitigate it; the discriminatory model continues recommending candidates. It is tempting because monitoring is a genuine fairness practise, yet it belongs to ongoing governance, whereas mitigation requires intervening in training data, features or objective before release.
- ✗
Collect more training data from the protected group
Why it's wrong here
Adding more data from the protected group can reinforce the historical patterns causing the disparity, since the labels themselves encode past discrimination. It is tempting because more representative data often improves fairness, but that works when underrepresentation is the cause, not biased labelling or proxy features.
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.