Courseiva
AI Governance and EthicshardMultiple ChoiceObjective-mapped

AI0-001 AI Governance and Ethics Practice Question

An AI model for skin cancer detection achieves high accuracy but performs poorly on dark skin tones. The team wants to evaluate whether the model is calibrated across skin tones. Which fairness metric should they use?

⚠ Common exam trap

The AI0-001 exam often tests the distinction between fairness metrics by presenting a scenario where 'accuracy' is high but subgroup performance differs, and candidates mistakenly choose equalized odds or demographic parity instead of recognizing that the core issue is confidence score reliability, i.e., calibration.

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

Calibration

Calibration is the correct metric because it directly measures whether the predicted probabilities of skin cancer match the actual outcomes across different skin tones. A model can have high overall accuracy but be miscalibrated for a subgroup if its confidence scores are systematically over- or under-confident for that group. In this scenario, the team needs to check if the model's risk scores are equally reliable for dark skin tones as for light skin tones, which is exactly what calibration assesses.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Equalised odds

    Why it's wrong here

    Equalized odds requires equal TPR and FPR across groups, not calibration.

  • Demographic parity

    Why it's wrong here

    Demographic parity would require equal positive prediction rates, not calibration.

  • Individual fairness

    Why it's wrong here

    Individual fairness is about similar individuals receiving similar predictions, not calibration.

  • Calibration

    Why this is correct

    Calibration checks that for a given predicted probability, the actual outcome rate is the same across groups.

About these practice questions

Courseiva writes every AI0-001 question from scratch — 754 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 →

How Courseiva writes practice questions · Editorial policy

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.