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
Other correlated features may still encode bias; removal is insufficient.
- ✓
Use a fairness-aware machine learning algorithm
Why this is correct
Fairness-aware algorithms incorporate constraints to reduce disparate impact.
- ✗
Analyze model predictions after deployment
Why it's wrong here
Post-hoc analysis only identifies bias; it does not prevent it.
- ✗
Collect more training data from the protected group
Why it's wrong here
More data does not inherently reduce bias if the underlying patterns are biased.
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.