AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A hospital deploys an AI system that predicts patient readmission risk within 30 days of discharge. The model uses features such as age, medical history, and treatment plans. The hospital discovers that the model has a significantly higher false positive rate for patients of a certain ethnic group compared to others, even though the model's overall accuracy is similar across groups. This disparity was not intentional. Which Microsoft responsible AI principle is most directly compromised?
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 requires AI systems to treat all groups equitably and avoid discrimination. A higher false positive rate for one ethnic group, even if unintentional, represents an unfair disparity. While Inclusiveness relates to designing for all people, Fairness specifically addresses equitable outcomes and bias mitigation, so it is the most directly compromised principle in this case.
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
Why this is correct
This is correct. Fairness in responsible AI requires that model outcomes—including errors—do not systematically disadvantage any demographic group. Here, the AI system produces disparate false positive rates across ethnic groups, meaning some groups are incorrectly flagged more often than others, which is a textbook fairness violation reflecting algorithmic bias in the prediction pipeline rather than a data breach or governance issue.
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Reliability and safety
Why it's wrong here
Incorrect. Reliability and safety focus on the system performing dependably and without causing harm. While a high false positive rate could cause harm, the core issue here is disparate impact across groups, which falls under fairness.
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Privacy and security
Why it's wrong here
This is incorrect. Privacy and security concerns focus on protecting sensitive patient data through encryption, access controls, data minimization, and compliance with regulations like HIPAA, all of which address unauthorized access or disclosure. The scenario describes no breach, exfiltration, or exposure of patient information; the problem is the model's unequal false positive rates, which is an output-quality and bias issue, not a data-protection failure.
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Accountability
Why it's wrong here
This is incorrect. Accountability in AI means establishing clear human ownership, audit trails, and governance so that decisions can be reviewed and responsible parties identified, and it would be relevant if the question asked who should answer for the system's actions. The stated concern is not about missing roles, documentation, or oversight but about a measurable pattern of disparate outcomes; fixing the error-rate imbalance is a fairness task, not merely an accountability task.
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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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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.