AI0-001 AI Security, Ethics and Governance Practice Question
Which THREE of the following are key principles of trustworthy AI as defined by major regulatory bodies?
⚠ Common exam trap
The AI0-001 exam often tests the distinction between ethical principles and business goals, so candidates mistakenly select 'maximum profitability' or 'proprietary secrecy' because they confuse corporate interests with regulatory requirements for trustworthy AI.
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
✓
Fairness and non-discrimination
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Fairness and non-discrimination
Why this is correct
Fairness and non-discrimination require AI systems to avoid unjustified disparate treatment or outcomes across protected groups. Regulatory frameworks including the EU AI Act and OECD principles treat this as a core trustworthy AI requirement, satisfying the stem's demand for key principles.
- ✓
Transparency and explainability
Why this is correct
Transparency and explainability require that AI decisions and their underlying logic be understandable and disclosed to affected parties. This is a foundational trustworthy AI principle in OECD and EU guidance, directly satisfying the stem's request for key regulatory principles.
- ✗
Maximum profitability
Why it's wrong here
Regulatory trustworthy-AI principles cover fairness, transparency, accountability and safety; profitability is a commercial objective with no place among them. It is tempting because AI projects must justify their cost, and profit would be a legitimate success metric in a business case, just not a trust principle.
- ✗
Proprietary secrecy
Why it's wrong here
Trustworthy AI frameworks require transparency, accountability and explainability, so concealing how a model works directly contradicts them. Proprietary secrecy is tempting because organisations do protect intellectual property and training data commercially, but that is a business concern, not a regulatory AI principle.
- ✓
Accountability
Why this is correct
Accountability requires clear assignment of responsibility for an AI system's outcomes and remedies when harm occurs. Recognised by the OECD and EU AI Act as a core trustworthy AI principle, it satisfies the stem's requirement for key principles.
About these practice questions
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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.