Question 402 of 1,000
Ethical Considerations of AIhardMultiple ChoiceObjective-mapped

AI Associate Transparency Practice Question

This AI Associate practice question tests your understanding of ethical considerations of ai. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. A key principle to apply: transparency. 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.

Exhibit

{
  "aiPolicy": {
    "fairnessChecks": false,
    "explainability": "none",
    "humanOversight": false
  }
}

Refer to the exhibit. A company uses this policy for a customer-facing AI model. What is the most critical ethical risk?

Exhibit

{
  "aiPolicy": {
    "fairnessChecks": false,
    "explainability": "none",
    "humanOversight": false
  }
}

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

Lack of transparency

The correct answer is D: Lack of transparency. The policy explicitly states 'explainability: none', which means the AI model's decisions cannot be explained or understood by users. This lack of transparency prevents users from knowing why a decision was made, challenging the decision, or trusting the system, making it the most critical ethical risk. Option A (Performance) is not indicated in the policy and is less critical than transparency. Option B (Data privacy) is important but the policy does not mention any privacy violations. Option C (Model accuracy) is not addressed in the policy and while important, transparency is more fundamental for ethical AI.

Key principle: Transparency

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Performance

    Why it's wrong here

    Performance is not indicated in the policy and is less critical than transparency. The policy focuses on explainability, not performance metrics.

  • Data privacy

    Why it's wrong here

    Data privacy is important but the policy does not mention any privacy violations, so it is not the most critical risk here.

  • Model accuracy

    Why it's wrong here

    Model accuracy is not addressed in the policy. While accuracy is important, transparency is more fundamental for ethical AI.

  • Lack of transparency

    Why this is correct

    Lack of transparency: The policy explicitly states 'explainability: none', meaning the AI model's decisions cannot be explained. This prevents users from understanding or challenging decisions, making it the most critical ethical risk.

    Related concept

    Transparency

Common exam traps

Common exam trap: answer the scenario, not the keyword

Candidates may confuse transparency with other ethical risks like privacy or accuracy, but the policy's explicit 'explainability: none' directly points to lack of transparency as the primary concern.

Detailed technical explanation

How to think about this question

Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.

KKey Concepts to Remember

  • Transparency
  • Explainability

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

Transparency

Real-world example

How this comes up in practice

A practitioner preparing for the AI Associate 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. Transparency 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.

Review transparency, then practise related AI Associate questions on the same topic to reinforce the concept.

Related practice questions

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FAQ

Questions learners often ask

What does this AI Associate question test?

Ethical Considerations of AI — This question tests Ethical Considerations of AI — Transparency.

What is the correct answer to this question?

The correct answer is: Lack of transparency — The correct answer is D: Lack of transparency. The policy explicitly states 'explainability: none', which means the AI model's decisions cannot be explained or understood by users. This lack of transparency prevents users from knowing why a decision was made, challenging the decision, or trusting the system, making it the most critical ethical risk. Option A (Performance) is not indicated in the policy and is less critical than transparency. Option B (Data privacy) is important but the policy does not mention any privacy violations. Option C (Model accuracy) is not addressed in the policy and while important, transparency is more fundamental for ethical AI.

What should I do if I get this AI Associate question wrong?

Review transparency, then practise related AI Associate questions on the same topic to reinforce the concept.

What is the key concept behind this question?

Transparency

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Last reviewed: Jun 23, 2026

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