AI0-001 AI Implementation and Operations Practice Question
A company deploys an AI model for loan approval. The model shows bias against a protected group. The team decides to use adversarial debiasing. What is the PRIMARY advantage of this approach?
⚠ Common exam trap
A common mix-up: candidates confuse 'reducing bias' with 'ensuring equal outcomes' (demographic parity), but adversarial debiasing targets equalized odds or equal opportunity by focusing on representation invariance, not strict rate equality.
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
✓
It reduces bias while preserving predictive performance by learning representations that are invariant to sensitive attributes.
Adversarial debiasing is an in-processing technique that trains a primary model to predict the target (e.g., loan approval) while simultaneously training an adversary to predict the sensitive attribute from the model's learned representations. The primary model is penalized when the adversary succeeds, forcing it to learn representations that are invariant to the sensitive attribute. This reduces bias while preserving predictive performance because the model retains the ability to learn task-relevant patterns that are not correlated with the protected attribute.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It guarantees the model's predictions are private.
Why it's wrong here
Privacy is not the primary goal of adversarial debiasing.
- ✓
It reduces bias while preserving predictive performance by learning representations that are invariant to sensitive attributes.
Why this is correct
This is the core benefit of adversarial debiasing.
- ✗
It is simpler to implement than pre-processing techniques.
Why it's wrong here
Adversarial training is complex and computationally intensive.
- ✗
It ensures equal approval rates across all groups.
Why it's wrong here
Adversarial debiasing aims for equal opportunity, not necessarily equal outcome.
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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.