AI0-001 AI Governance and Ethics Practice Question
A company uses an AI model to screen job applicants. A disparate impact analysis reveals that the model's rejection rate for a protected group is significantly higher than for others. Which THREE actions should the company take to address this?
⚠ Common exam trap
The AI0-001 exam often tests the misconception that removing protected attributes (option C) is sufficient to eliminate bias, when in reality it can hide bias and still allow proxy discrimination, making it an incomplete and sometimes counterproductive solution.
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
✓
Revisit training data for historical bias and consider reweighting
Revisiting the training data for historical bias and applying reweighting directly addresses the root cause of disparate impact. If the training data contains biased labels or skewed representation of the protected group, the model will learn and amplify those biases. Reweighting adjusts the loss function to give more importance to underrepresented or disadvantaged groups, helping to equalize error rates across groups.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Revisit training data for historical bias and consider reweighting
Why this is correct
Addressing data bias is a fundamental step to reduce disparate impact.
- ✗
Ignore the disparity because the model is accurate overall
Why it's wrong here
Ignoring disparity may violate anti-discrimination laws.
- ✗
Remove all demographic attributes from the dataset
Why it's wrong here
Removing attributes may not eliminate bias due to proxy variables, and can hinder fairness analysis.
- ✓
Apply fairness constraints or adversarial debiasing during training
Why this is correct
Fairness-aware training algorithms can directly reduce bias.
- ✓
Consider using a different model that achieves better fairness metrics
Why this is correct
Switching to a fairer model is a viable mitigation strategy.
About these practice questions
This AI0-001 question is part of Courseiva's 754-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam 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.