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AI Security, Ethics and GovernancemediumMultiple ChoiceObjective-mapped

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

    Explanation alone does not correct the bias; the model needs retraining.

  • Reduce the sample size of non-native English speakers to balance the dataset

    Why it's wrong here

    Reducing data can increase bias and reduce model performance.

  • Continue using the model as is, since overall accuracy is acceptable

    Why it's wrong here

    Ignoring biased outcomes is unethical and could lead to disparate impact.

  • Retrain the model with a more representative dataset that includes diverse language backgrounds

    Why this is correct

    Retraining with balanced data addresses the root cause of bias.

About these practice questions

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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.