AI0-001 AI Security, Ethics and Governance Practice Question
An AI system used for resume screening is found to consistently reject female candidates for technical roles. The data science team retrains the model after removing the 'gender' feature, but the bias persists. What is the most likely cause?
⚠ Common exam trap
The AI0-001 exam often tests the concept that simply removing a protected attribute is insufficient to eliminate bias, because proxy variables can act as surrogates, and candidates mistakenly think retraining on the same data without the feature will solve the problem.
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
✓
The model uses proxy variables that correlate with gender
Even after removing the explicit 'gender' feature, the model can still learn biased patterns from proxy variables that correlate strongly with gender, such as years of experience (which may be lower for women due to career breaks), educational institutions attended, or even hobbies listed on resumes. These proxies act as surrogates for the protected attribute, allowing the model to effectively discriminate despite the feature being removed. This is a well-known phenomenon in algorithmic fairness called 'redundant encoding' or 'proxy discrimination.'
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model architecture is too complex
Why it's wrong here
Complexity doesn't inherently cause bias; it may capture more relationships, but the core issue is proxy variables.
- ✓
The model uses proxy variables that correlate with gender
Why this is correct
Features like 'years of experience gaps' or 'extracurricular activities' may correlate with gender and perpetuate bias.
- ✗
The training data still contains historical hiring bias
Why it's wrong here
While true, the question is about why bias persists after removing gender. Proxies are the more direct cause.
- ✗
The evaluation metric does not measure fairness
Why it's wrong here
Evaluation metric is important but not the cause of bias persistence; the model still learns biased patterns.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 754 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.