AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A startup develops an AI system that uses images of skin lesions to diagnose skin cancer. The model is trained exclusively on images from dermatology clinics in North America, which primarily feature lighter skin tones. When the system is deployed globally via a mobile app, it shows high accuracy for lighter skin tones but significantly lower accuracy for darker skin tones. Which Microsoft responsible AI principle is most directly violated?
⚠ Common exam trap
Many exam-takers confuse inclusiveness with reliability, thinking that lower accuracy for some groups is a reliability issue, but the principle of inclusiveness specifically addresses fairness and performance across all user groups, not just system uptime or error rates in general.
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
✓
B. Inclusiveness
Inclusiveness. The model was trained exclusively on images from North American dermatology clinics, which primarily feature lighter skin tones, leading to significantly lower accuracy for darker skin tones. This directly violates the inclusiveness principle, which requires AI systems to be designed for and perform well across all user groups, regardless of skin tone or other demographic characteristics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A. Reliability and Safety
Why it's wrong here
While the system is less reliable for darker skin tones, the primary violation is that it fails to serve those users adequately, which is a matter of inclusiveness. Reliability and Safety focuses on overall dependability and harm prevention.
- ✓
B. Inclusiveness
Why this is correct
Inclusiveness is one of Microsoft's core responsible AI principles, requiring systems to equitably serve all user populations regardless of skin tone, ethnicity, or other personal attributes. The model's poorer diagnostic accuracy for darker skin lesions likely stems from training data that is not representative, creating algorithmic bias that excludes these users from equally reliable care. This direct performance disparity is fundamentally a failure of inclusive design, rather than a secondary concern about safety, privacy, or explainability.
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C. Privacy and Security
Why it's wrong here
Privacy and Security focus on safeguarding sensitive personal data—such as dermatological images—from unauthorized access, leakage, or misuse, often through encryption, access controls, and compliance with regulations like HIPAA or GDPR. The scenario describes no data breach, consent violation, or insecure storage; it centers on an accuracy gap across skin tones. Thus, while the image data is confidential, the underlying violation is not a confidentiality issue but a fairness issue.
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D. Transparency
Why it's wrong here
Transparency means clearly communicating an AI system's capabilities, limitations, and underlying logic to users, often via documentation, interpretability tools, or confidence scores. Although openly acknowledging the darker-skin performance gap could inform users and partially manage expectations, transparency alone does nothing to improve the model's actual accuracy for that group. Consequently, transparency is at best a mitigating disclosure, not the principle that is directly violated by the unequal diagnostic performance.
Go deeper
Related to this question
Learn chapter
Responsible AI Principles
Key term
Inclusiveness
Inclusiveness in IT means designing systems, software, and workflows so that they are accessible and usable by people with a wide range of abilities, backgrounds, and needs.
Key term
Feature
A feature is a distinct unit of functionality that delivers value to the user, often managed and tracked throughout the software development lifecycle.
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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.