Core Principles of AI Ethics
Which THREE of the following are key principles of AI ethics as defined by major frameworks?
Quick Answer
The answer is fairness, transparency, and accountability. These three are the core principles of AI ethics because they directly address the moral and governance concerns of AI systems: fairness prevents algorithmic bias, transparency ensures decisions can be understood and audited, and accountability establishes clear responsibility for outcomes. On the CompTIA AI+ AI0-001 exam, this question tests your ability to distinguish ethical guardrails from technical performance metrics—a common trap is confusing scalability or latency, which are about system speed and capacity, with ethics. To remember the trio, think of the acronym FTA: Fairness, Transparency, Accountability—these are the pillars that keep AI trustworthy, not just fast.
⚠ Common exam trap
The AI0-001 exam often tests candidates by mixing technical performance metrics (scalability, latency) with ethical principles, expecting you to recognize that only value-based concepts like transparency, accountability, and fairness belong to AI ethics frameworks.
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
✓
Transparency
Transparency is a core principle of AI ethics because it requires that AI systems be open about their purpose, data sources, and decision-making processes. Major frameworks like the OECD AI Principles and the EU Ethics Guidelines for Trustworthy AI emphasize transparency to enable auditability and informed consent. Without transparency, stakeholders cannot verify that an AI system operates as intended or identify potential biases.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Transparency
Why this is correct
Transparency is about openness in AI systems' workings and decisions.
- ✗
Scalability
Why it's wrong here
Scalability is a technical requirement, not an ethical principle.
- ✓
Accountability
Why this is correct
Accountability means taking responsibility for AI system outcomes.
- ✗
Latency
Why it's wrong here
Latency is a performance measure, not ethics.
- ✓
Fairness
Why this is correct
Fairness ensures unbiased and equitable outcomes.
About these practice questions
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Same concept, more angles
1 more way this is tested on AI0-001
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which THREE of the following are key principles of trustworthy AI as defined by major regulatory bodies?
medium- ✓ A.Fairness and non-discrimination
- ✓ B.Transparency and explainability
- C.Maximum profitability
- D.Proprietary secrecy
- ✓ E.Accountability
Why A: Fairness and non-discrimination (A) is a core principle of trustworthy AI because regulatory bodies like the European Commission's High-Level Expert Group on AI and the OECD require that AI systems do not perpetuate or amplify biases against protected groups. This involves implementing bias detection and mitigation techniques during model training and validation, such as using fairness metrics like demographic parity or equalized odds to ensure equitable outcomes across different demographic segments.
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.