The answer is transparency and explainability. This principle is correct because the core issue is a lack of visibility into why the AI system assigned different prices to two customers for the same product at the same time; by redesigning the algorithm to provide clear, understandable reasons for price variations—such as demand, purchase history, or time of day—the company directly addresses the customer’s inability to scrutinize the decision-making process, which is essential for resolving fairness complaints in dynamic pricing. On the CompTIA AI+ AI0-001 exam, this question tests your ability to distinguish between ethical principles like fairness, accountability, and transparency; a common trap is choosing “fairness” alone, but the prompt specifically highlights the customer’s confusion over the *why* behind the price difference, making transparency and explainability the precise remedy. A helpful memory tip is to think of the “Why Factor”: if a user cannot ask “why did this happen?” and get a clear answer, the missing principle is transparency and explainability.
AI0-001 AI Security, Ethics and Governance Practice Question
This AI0-001 practice question tests your understanding of ai security, ethics and governance. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
An e-commerce company uses an AI system to set dynamic prices for products. A customer complains that the price they see is higher than the price shown to a friend for the same product at the same time. The company wants to ensure pricing fairness. Which ethical principle should guide the redesign of the pricing algorithm?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Transparency and explainability
Transparency and explainability is the correct principle because the core issue is that the customer cannot understand why the AI system set a different price for them compared to their friend. Redesigning the algorithm to provide clear, understandable reasons for price variations—such as demand, purchase history, or time of day—directly addresses this lack of visibility. This principle ensures that the system's decision-making process is open to scrutiny, which is essential for building trust and resolving fairness complaints in dynamic pricing models.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 and explainability
Why this is correct
Transparency requires the company to disclose how prices are determined, helping to ensure fairness and build trust.
Related concept
Read the scenario before looking for a memorised answer.
✗
Privacy by design
Why it's wrong here
Privacy by design focuses on data protection, not on ensuring fair pricing outcomes.
✗
Accountability
Why it's wrong here
Accountability assigns responsibility but does not directly address the fairness of the pricing algorithm.
✗
Beneficence
Why it's wrong here
Beneficence focuses on promoting good, but does not specifically prevent unfair pricing practices.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the distinction between 'accountability' (who is responsible) and 'transparency' (how the decision is made), leading candidates to pick accountability when the question explicitly asks for the principle that guides the redesign to ensure fairness through understanding.
Detailed technical explanation
How to think about this question
Under the hood, transparency in AI pricing algorithms often involves implementing model-agnostic interpretability techniques like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) to generate feature importance scores for each price output. For example, a real-world scenario might involve an airline pricing engine that uses a gradient-boosted decision tree; without transparency, a customer cannot know that their price was higher due to a feature like 'browser type' or 'previous purchase frequency,' which could be perceived as unfair. The redesign would require logging the top contributing features for each price quote and presenting them in a user-friendly dashboard or explanation.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
AI Security, Ethics and Governance — This question tests AI Security, Ethics and Governance — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Transparency and explainability — Transparency and explainability is the correct principle because the core issue is that the customer cannot understand why the AI system set a different price for them compared to their friend. Redesigning the algorithm to provide clear, understandable reasons for price variations—such as demand, purchase history, or time of day—directly addresses this lack of visibility. This principle ensures that the system's decision-making process is open to scrutiny, which is essential for building trust and resolving fairness complaints in dynamic pricing models.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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