AI0-001 AI Governance and Ethics Practice Question
A data scientist notices that a hiring model systematically scores female candidates lower than male candidates with similar qualifications. The training data was collected from past hiring decisions where the company historically hired more men. Which type of AI bias is most directly demonstrated?
⚠ Common exam trap
The AI0-001 exam often tests the distinction between historical bias (data-driven) and algorithmic bias (model-driven), and the trap here is that candidates confuse 'algorithmic bias' as the catch-all term, missing that the root cause is the historical data, not the algorithm's logic.
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, because the model's lower scoring of female candidates stems directly from training data that reflects past hiring decisions where the company historically hired more men. This bias is embedded in the data itself, not introduced by the algorithm or data collection method. Historical bias occurs when the training data encodes societal or organizational prejudices from the past, which the model then perpetuates.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Selection bias
Why it's wrong here
Selection bias concerns non-random sampling that makes a dataset unrepresentative of the target population, such as surveying only volunteers. Here the training data faithfully records the company's actual past hiring decisions, so the disparity stems from historical outcomes themselves, not from how samples were selected into the dataset.
- ✗
Algorithmic bias
Why it's wrong here
Algorithmic bias describes bias arising from the algorithm's own design or optimisation, not from skewed historical training labels. It is tempting because the model's scoring logic is implicated, but here the disparity traces to the training data's historical hiring imbalance, which is the defining mechanism of the correct answer.
- ✗
Confirmation bias
Why it's wrong here
Confirmation bias is a human cognitive tendency to favour information confirming existing beliefs, not a property of training data distributions. It tempts because biased hiring reflects pre-existing attitudes, but the model inherits the imbalance statistically from historical decisions rather than from any human's selective interpretation during analysis.
- ✓
Historical bias
Why this is correct
Historical bias arises when training data reflects past human prejudice, so the model reproduces that pattern. Here, past hiring favoured men, embedding that imbalance in the labels. This directly satisfies the stem's constraint of skewed historical hiring decisions, producing lower scores for equally qualified female candidates.
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.