AI0-001 AI Security, Ethics and Governance Practice Question
An AI system used for autonomous driving is found to have a lower accuracy in detecting pedestrians with darker skin tones. The development team wants to address this ethical issue. Which action is most effective?
⚠ Common exam trap
CompTIA often tests the misconception that bias can be fixed by simply changing the algorithm or threshold, when in reality the most effective first step is to address data imbalance through targeted augmentation.
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
✓
Augment the training dataset with more images of pedestrians with darker skin
Augmenting the training dataset with more images of pedestrians with darker skin directly addresses the root cause of the bias: underrepresentation in the training data. By providing a more balanced and diverse dataset, the model can learn more robust features for all skin tones, reducing accuracy disparity without altering the algorithm's core logic or introducing arbitrary thresholds.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Conduct additional testing to measure the disparity
Why it's wrong here
Measuring the disparity quantifies the problem but changes nothing in the model or data, so the accuracy gap persists in deployment. It is tempting because measurement is a defensible first step, and it would be correct when auditing an existing system or establishing a baseline before remediation.
- ✓
Augment the training dataset with more images of pedestrians with darker skin
Why this is correct
Adding more darker-skinned pedestrian images directly corrects the class imbalance in the training distribution, which is the root cause of the disparate accuracy. The model learns underrepresented features only when sufficient examples exist, so dataset augmentation reduces the bias more effectively than post-hoc threshold tuning or documentation.
- ✗
Replace the object detection algorithm with a different one
Why it's wrong here
Swapping the detection algorithm does not address the training data imbalance that produced the disparity, and the replacement may inherit the same bias. It is tempting because it appears to attack the model directly, and it would be correct when the current architecture itself limits achievable accuracy on a balanced dataset.
- ✗
Adjust the model's decision threshold for pedestrian detection
Why it's wrong here
Shifting the decision threshold trades false positives against false negatives across all groups; it cannot correct the underlying feature representation that causes darker-skinned pedestrians to be missed. It is tempting because it is a quick post-processing tweak, and it would be correct for calibrating precision and recall on an already-representative dataset.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 962 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.