Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped

AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations

A bank uses an AI system to approve or deny personal loan applications. Several customers whose loans were denied have asked for an explanation of why their application was rejected. Which Microsoft responsible AI principle requires the bank to provide understandable reasons for the AI's decision?

⚠ Common exam trap

It's easy for candidates to confuse transparency with fairness, thinking that explaining a decision inherently ensures it is fair, but transparency only requires the explanation to be provided, not that the decision itself is unbiased.

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 the Microsoft responsible AI principle that requires AI systems to be understandable and interpretable. In this scenario, the bank must provide clear, understandable reasons for loan denials, which directly aligns with transparency's goal of enabling users to understand how and why decisions are made. This principle ensures that AI outcomes are not opaque black-box decisions but can be explained in human terms.

Answer analysis

Option-by-option breakdown

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

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Reliability and safety
Why it's wrong here
Reliability and safety focus on a system's operational integrity: it must function consistently, fail gracefully, and avoid harmful actions, such as denying loans due to a software bug or serving incorrect predictions. These principles are about performance and risk mitigation, not about articulating why a specific decision was made. An AI system can be highly reliable and safe yet still produce unexplainable loan denials, so this option does not address the requirement to explain individual decisions.
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Fairness
Why it's wrong here
Fairness addresses whether the model treats protected groups equitably by measuring bias metrics, setting threshold parity, and applying de-biasing techniques. Equitable outcomes could be achieved without providing any individual reasoning to applicants, because fairness is evaluated at a group or population level. While explainability can help audit fairness, the explicit obligation to give a meaningful reason for each denial falls under transparency rather than fairness.
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Transparency
Why this is correct
Transparency requires that the AI system's decisions can be understood and described in human-meaningful terms, which directly addresses the bank's need to explain why a loan was approved or denied. This includes using interpretable model architectures, or model-agnostic explainability methods like LIME or SHAP, to generate per-decision rationales. Regulatory frameworks increasingly expect transparency in automated lending, so the correct principle for decision-level explanations is transparency.
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Privacy and security
Why it's wrong here
Privacy and security govern how applicant data is collected, stored, encrypted, and shared, ensuring protection against unauthorized access, breaches, and misuse. These measures prevent unauthorized parties from seeing loan data but do not require the AI itself to reveal its reasoning. A fully private and secure system could still issue opaque decisions, so this principle does not mandate that the bank explain individual approval or denial outcomes.

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AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations

Why each option matters

Option-by-option breakdown

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