AI0-001 AI Governance and Ethics Practice Question
An AI system trained on historical medical records shows that certain racial groups have higher predicted risk for a disease. The data reflects real-world differences in diagnosis rates due to unequal access to healthcare. Which type of bias is this?
⚠ Common exam trap
AI0-001 often tests the distinction between historical bias and other bias types by presenting a scenario where data accurately reflects real-world disparities, tempting candidates to choose algorithmic or selection bias when the root cause is societal inequity embedded in the data.
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
✓
Historical bias
Historical bias occurs when training data reflects pre-existing societal inequalities or biases, even if the data is accurately collected and representative. Here, the medical records show real-world differences in diagnosis rates due to unequal healthcare access, meaning the data itself encodes a historical inequity. The model learns and perpetuates this pattern, predicting higher risk for certain racial groups based on biased historical outcomes rather than true biological differences. Thus, the bias is inherent in the data's origin, not in the algorithm or sampling method.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Algorithmic bias
Why it's wrong here
Algorithmic bias arises from the model's design, feature weighting or optimisation choices, not from skewed training data. It is tempting because the model produces the biased output, but the stem attributes the disparity to historical records reflecting unequal healthcare access, which is a data-sourcing problem.
- ✗
Selection bias
Why it's wrong here
Selection bias occurs when the sampled population is unrepresentative of the target population, for example excluding patients who never reached a clinic. Here the records reflect genuine diagnosis-rate differences caused by unequal access, so the data is skewed in its labels rather than its sampling frame.
- ✓
Historical bias
Why this is correct
Historical bias arises when training data reflects past societal inequities, such as unequal healthcare access producing differing diagnosis rates. The model learns and reproduces those existing disparities, which is precisely the real-world diagnostic inequality described in the scenario.
- ✗
Confirmation bias
Why it's wrong here
Confirmation bias describes people favouring information that supports existing beliefs, not disparities inherited from training records. It is tempting because bias appears in the predictions, but no human interpretation is involved; the stem describes historical diagnosis data reflecting unequal access, which is a data-origin issue.
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 and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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.