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 (A) is a core AI-ethics principle because major frameworks require that AI systems' capabilities, limitations, data use, and decision logic be disclosed and explainable to affected stakeholders. Accountability (C) is likewise fundamental, as frameworks such as the OECD AI Principles and UNESCO Recommendation demand that responsibility for AI outcomes be clearly assigned to identifiable humans or organizations, with mechanisms for redress. Fairness (E) is a third key principle, requiring that AI systems avoid unjust bias and discrimination and treat individuals and groups equitably across the lifecycle. By contrast, scalability (B) is an engineering and performance concern about handling growing workloads, and latency (D) is a technical metric of response delay; neither is an ethical principle in AI frameworks.
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 requires that AI systems' capabilities, limitations, and decision processes be openly disclosed to stakeholders. Major frameworks including the OECD AI Principles and UNESCO recommendations list it as a core ethical principle, enabling informed use and accountability.
- ✗
Scalability
Why it's wrong here
Scalability is an engineering and operational property of systems, not a normative principle governing how AI should behave. Ethics frameworks address fairness, transparency, accountability, privacy and human oversight. Scalability belongs in architecture and capacity planning discussions, where it is a legitimate design goal.
- ✓
Accountability
Why this is correct
Accountability assigns clear responsibility for an AI system's outcomes to identifiable people or organisations. Recognised in frameworks such as the OECD AI Principles and NIST AI RMF, it ensures harms can be traced, remedied, and governed rather than left unattributed.
- ✗
Latency
Why it's wrong here
Latency measures response time and is a performance characteristic, not a normative principle guiding AI conduct. Ethics frameworks cover fairness, transparency, accountability, privacy and safety. Latency is correctly evaluated in performance benchmarking and service-level objectives, not in ethical review.
- ✓
Fairness
Why this is correct
Fairness requires AI systems to avoid unjust discrimination and treat individuals and groups equitably. It appears as a core principle in the OECD AI Principles, UNESCO recommendations, and NIST AI RMF, addressing bias in training data and outputs.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 962 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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 because trustworthy AI frameworks such as the EU AI Act and OECD AI Principles require systems to avoid biased outcomes and unjust discrimination across protected groups. Transparency and explainability (B) is also correct, as these bodies mandate that AI decisions be understandable and that stakeholders can access meaningful information about how systems operate. Accountability (E) is correct because trustworthy AI requires clear responsibility and redress mechanisms, ensuring that developers and deployers can be held answerable for system outcomes. Maximum profitability (C) is not a trustworthiness principle; it is a business objective and is not part of regulatory AI ethics definitions. Proprietary secrecy (D) is also not a principle, since trustworthiness frameworks emphasize disclosure, auditability, and transparency rather than concealment.
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.