AI0-001 AI Security, Ethics and Governance Practice Question
A healthcare startup deploys an AI model to predict patient readmission rates. An internal audit reveals that the model consistently underestimates readmission risk for non-native English speakers. According to AI ethics principles, what is the most appropriate course of action?
⚠ Common exam trap
The AI0-001 exam often tests the misconception that adding a disclaimer or adjusting sample sizes post-hoc is sufficient to address bias, when in fact the ethical requirement is to fix the data or model at the training stage to ensure fairness.
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
✓
Retrain the model with a more representative dataset that includes diverse language backgrounds
It directly addresses the root cause of the bias: the training data lacks sufficient representation from non-native English speakers, leading to systematic underestimation of readmission risk for that group. Retraining with a more representative dataset aligns with the AI ethics principle of fairness by ensuring the model learns patterns across all demographic groups equally, rather than masking the issue with disclaimers or manipulating sample sizes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add a confidence score disclaimer to model outputs
Why it's wrong here
Adding a confidence disclaimer leaves the underlying bias untouched: the model still computes predictions from features that underrepresent non-native English speakers, so the disparity persists. Disclaimers suit communicating uncertainty in otherwise sound models. Here the audit demands remediating the training data or features driving the systematic underestimation.
- ✗
Reduce the sample size of non-native English speakers to balance the dataset
Why it's wrong here
Removing non-native English speakers destroys the evidence of bias and worsens representation, breaching fairness and inclusivity principles. Reducing sample size is tempting as dataset balancing, but balancing corrects class imbalance, not underrepresentation of a protected subgroup.
- ✗
Continue using the model as is, since overall accuracy is acceptable
Why it's wrong here
Aggregate accuracy masks subgroup harm; continuing deployment perpetuates unfair underestimation of readmission risk for non-native English speakers. Accepting overall accuracy is tempting when metrics look healthy, but ethics requires evaluating performance across demographic groups, not just the aggregate.
- ✓
Retrain the model with a more representative dataset that includes diverse language backgrounds
Why this is correct
Retraining with a representative dataset addresses the root cause: the model's bias stems from training data lacking diverse language backgrounds. This satisfies the fairness principle of equitable performance across groups, rather than merely masking the disparity through post-hoc adjustments.
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.