AI0-001 AI Governance and Ethics Practice Question
A data scientist is training a resume screening model to rank job applicants. The training data includes historical hiring decisions from the past 10 years. The company wants to avoid unfair bias against underrepresented groups. Which type of bias is most likely present in the training data?
⚠ Common exam trap
AI0-001 often tests the distinction between historical bias (bias baked into the data by past human decisions) and algorithmic bias (bias from model design) — candidates frequently pick 'algorithmic bias' whenever a model produces unfair outcomes, regardless of root cause.
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 past human decisions or societal patterns that were themselves biased, causing the model to learn and perpetuate those patterns. A resume screening model trained on 10 years of hiring decisions will inherit whatever demographic skews existed in those decisions.
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 emerges from model design, feature engineering or optimisation choices during development, not from the training labels themselves. It would apply if the model's construction introduced skew; the stem describes biased historical hiring decisions already recorded in the data, which is historical bias.
- ✗
Selection bias
Why it's wrong here
Selection bias arises when the sampling or selection process skews which records enter the dataset, not when historical outcomes themselves encode past discrimination. It would fit if applicants were filtered before inclusion; here the labels reflect biased past hiring decisions, which is historical bias.
- ✗
Confirmation bias
Why it's wrong here
Confirmation bias describes people favouring information that supports existing beliefs during analysis or interpretation. It would fit if the data scientist selectively interpreted results; the stem instead concerns discriminatory patterns embedded in ten years of recorded hiring outcomes, which is historical bias.
- ✓
Historical bias
Why this is correct
Historical bias arises because the model learns from a decade of past hiring decisions that already reflect prior discriminatory outcomes, so the training labels themselves encode the unfair patterns the company now wants to avoid.
About these practice questions
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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.