AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A bank is developing an AI system to automatically approve or reject small personal loans. To ensure the system treats applicants fairly regardless of race, gender, or age, which Microsoft responsible AI principle is most directly relevant?
⚠ Common exam trap
Microsoft often tests the distinction between Fairness and Inclusiveness, where candidates mistakenly choose Inclusiveness because they think it covers all aspects of ethical AI, but Fairness is the specific principle for preventing discrimination in automated decisions.
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
The Fairness principle is directly relevant because it requires AI systems to treat all individuals equitably, avoiding discrimination based on protected attributes like race, gender, or age. In this loan approval scenario, the system must be designed and tested to ensure its decisions do not systematically disadvantage any group, which is the core goal of fairness in AI.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Inclusiveness
Why it's wrong here
Inclusiveness focuses on designing AI that is usable and accessible by people of all abilities, languages, and cultural contexts, such as incorporating assistive technology or multilingual interfaces. While inclusive design can help identify and reduce certain biases by broadening the range of users considered, the principle itself is concerned with access and usability, not with preventing disparate outcomes in consequential decisions like loan approvals. Therefore, Inclusiveness is not the primary principle for ensuring non-discrimination in the bank's AI system.
- ✓
Fairness
Why this is correct
Fairness is the responsible AI principle that directly targets systematic bias and discrimination by requiring equitable treatment across demographic groups such as race, gender, age, or income. In loan approval, fairness demands that the AI model does not disproportionately deny or grant credit to any protected group, often evaluated using metrics like demographic parity or equalized odds. This principle is uniquely suited to the bank's scenario because it explicitly addresses the risk of unintentional discrimination in automated decisions, making it the correct choice.
- ✗
Reliability and safety
Why it's wrong here
Reliability and safety concern whether the AI system performs accurately, consistently, and robustly under expected and unexpected conditions, such as handling missing data, outliers, or malicious inputs. In lending, these attributes ensure that the approval system does not crash or produce erratic outputs, but they do not inherently prevent biased decisions against specific demographic groups. A perfectly reliable and safe system can still systematically discriminate, which is why this principle is not the one that addresses non-discrimination in loan approvals.
- ✗
Transparency
Why it's wrong here
Transparency centers on making the AI's decision-making process observable and understandable, so stakeholders can see why a loan was approved or rejected and detect potential problems. It supports fairness by enabling audits of the model's behavior, but transparency alone does not enforce equitable treatment—it merely provides visibility into whatever biases may already exist. For the bank's objective of avoiding discrimination, transparency is a complementary enabler rather than the guiding principle, so it is not the correct answer.
Go deeper
Related to this question
Learn chapter
Responsible AI Principles
Key term
Responsible AI
A framework of ethical principles and practices that ensure artificial intelligence systems are developed and deployed in a transparent, fair, accountable, and safe manner.
Key term
Fairness
Fairness in AI means designing and deploying machine learning models that do not produce biased outcomes against any group of people based on protected characteristics like race, gender, or age.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft 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 AI-900 exam.