AI0-001 AI Governance and Ethics Practice Question
A social media platform uses an AI system to moderate content. The system incorrectly flags legitimate posts as hate speech, disproportionately affecting minority groups. Which type of bias is likely present?
⚠ Common exam trap
The AI0-001 exam often tests the distinction between 'algorithmic bias' (bias introduced by the model's design or deployment) and 'historical bias' (bias present in the training data), so candidates mistakenly choose historical bias when the question describes a system that actively produces unfair outcomes due to its own 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
✓
Algorithmic bias
The AI system's output (incorrectly flagging legitimate posts as hate speech) is a direct result of the model's design, training data, or deployment choices, which is the definition of algorithmic bias. This bias disproportionately affects minority groups because the algorithm's decision-making process systematically produces unfair outcomes for those groups, even if the training data itself was not historically biased.
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 this is correct
Algorithmic bias arises when the model's design, training data, or optimisation produces systematically unfair outcomes for particular groups. The disproportionate flagging of minority users' legitimate posts is a direct manifestation of that bias embedded in the classifier's decision logic.
- ✗
Historical bias
Why it's wrong here
Historical bias arises when training data reflects past societal prejudice, producing a model that inherits those skewed patterns. It is tempting here because disproportionate flagging of minority-group content matches that symptom, but the stem's specific mechanism — legitimate posts mislabelled as hate speech — points to label or annotation bias in the training set rather than historical under-representation alone.
- ✗
Selection bias
Why it's wrong here
Selection bias concerns non-representative sampling during data collection, so it would show as skewed training coverage rather than the deployed classifier disproportionately flagging minority-group posts. It is tempting because imbalanced sampling can seed downstream skew, but the stem describes systematic misclassification of legitimate content, which is a labelling or historical bias symptom, not sampling methodology.
- ✗
Confirmation bias
Why it's wrong here
Confirmation bias describes a human analyst favouring evidence that supports existing beliefs, not a classifier systematically mislabelling minority-group posts. It is tempting because reviewers auditing flagged content may seek confirming examples, but the stem describes skewed model outputs across demographic groups, which points to training-data or label bias rather than the reviewer's reasoning.
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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.