AI0-001 AI Governance and Ethics Practice Question
A company deploys an AI resume screening tool. It learns from historical hiring data where most successful hires were male, leading the model to favour male candidates. Which type of bias is this primarily?
⚠ Common exam trap
The CompTIA AI exam often tests the distinction between historical bias and algorithmic bias, where candidates mistakenly attribute the problem to the algorithm itself rather than recognizing that the bias was already present in the training 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
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Historical bias
The model learned from historical hiring data that already contained a gender imbalance, where most successful hires were male. This is a classic case of historical bias, where the training data reflects past societal or organizational biases, and the AI system perpetuates those biases in its predictions. The bias originates in the data, not in the model's algorithm or the 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.
- ✓
Historical bias
Why this is correct
Historical bias arises when training data reflects past societal inequities, so the model reproduces them. Here the historical hiring data over-represents male successes, and the model learns that pattern, favouring male candidates. This directly satisfies the stem's constraint: bias originating from skewed historical outcomes rather than sampling or labelling errors.
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Confirmation bias
Why it's wrong here
Confirmation bias describes people favouring information confirming existing beliefs, not a model learning from training labels. The tool's preference stems from patterns in historical hiring outcomes, which is historical bias. Confirmation bias would apply if reviewers accepted only outputs matching their preconceptions.
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Algorithmic bias
Why it's wrong here
Algorithmic bias is the broad umbrella term covering many causes, so it does not name the specific mechanism here. The model reproduces a gender skew present in its historical training labels, which is historical bias. Algorithmic bias would be the answer if the disparity arose from the algorithm's own design choices.
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Selection bias
Why it's wrong here
Selection bias concerns how samples are chosen, such as surveying only current employees. The model instead reproduces a historical pattern in its training labels, which is the defining mechanism of historical bias. Selection bias would apply if the training data itself under-represented women.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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