AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A hospital deploys an AI diagnostic system that achieves 95% accuracy overall. However, for patients from a specific minority ethnic group, the accuracy drops to 60%. The hospital decides to continue using the system because the overall accuracy is acceptable. Which Microsoft responsible AI principle is most directly violated by this decision?
⚠ Common exam trap
Test-takers frequently confuse 'overall accuracy' with 'system quality' and fail to recognize that Fairness requires equal performance across all subgroups, not just a high average.
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 decision to continue using the system despite a 60% accuracy for a minority ethnic group directly violates the Fairness principle. Fairness requires that AI systems treat all groups equitably and avoid discrimination, even if overall metrics are high. A 35% accuracy gap between groups indicates systemic bias, which the hospital is ignoring by prioritizing aggregate performance over equitable outcomes.
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
Fairness in AI requires that a system's predictive performance be consistent across demographic groups, often operationalized via metrics like equalized odds or demographic parity. Here, a 95% overall diagnostic accuracy masks a significantly lower accuracy for a minority group, meaning the system fails the core fairness principle of non-discrimination in outcomes. This is not about systemic intent but about measurable disparate impact, which Microsoft's responsible AI framework explicitly identifies as a fairness violation.
- ✗
Inclusiveness
Why it's wrong here
Inclusiveness focuses on making AI usable by people with a broad range of abilities and needs, including those with visual, hearing, cognitive, or motor impairments. The scenario describes an accuracy disparity across ethnic groups, not an issue of accessibility or universal design. Even if the system were fully inclusive in its interface and support for assistive technologies, the underlying biased diagnostic performance would remain a separate and more fundamental defect.
When this WOULD be correct
Inclusiveness would be correct if the question described a system that fails to accommodate users with disabilities (e.g., no support for screen readers) or excludes certain user groups from the design process, rather than having disparate accuracy across ethnic groups.
- ✗
Transparency
Why it's wrong here
Transparency requires disclosing how an AI system works, including its training data, limitations, and decision-making processes, to enable appropriate trust and oversight. While the hospital might be transparent about the system's overall 95% accuracy, transparency alone does not address the systematic underperformance for a minority group. The core violation is the unequal treatment inherent in the predictions, not a lack of information about those predictions.
When this WOULD be correct
Transparency would be correct if the question described a system that hides its decision-making process, such as a black-box model used for loan approvals without explaining why a loan was denied, and the organization fails to provide any documentation or reasoning.
- ✗
Accountability
Why it's wrong here
Accountability means establishing clear ownership, governance, and remediation processes so that an organization takes responsibility for AI outcomes. Although the hospital should be accountable for deploying and monitoring the system, the principle most directly violated is fairness because the harm is the unequal diagnostic accuracy itself. Accountability would be a necessary follow-up action—such as auditing and correcting the bias—but it is not the specific ethical principle that the system's behavior breaches.
When this WOULD be correct
A company deploys an AI system that makes hiring decisions, but there is no clear process for auditing the system's decisions or addressing errors. The company cannot explain who is responsible for the system's outcomes. This would violate the accountability principle.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓FairnessCorrect answer▾
Why this is correct
Fairness in AI requires that a system's predictive performance be consistent across demographic groups, often operationalized via metrics like equalized odds or demographic parity. Here, a 95% overall diagnostic accuracy masks a significantly lower accuracy for a minority group, meaning the system fails the core fairness principle of non-discrimination in outcomes. This is not about systemic intent but about measurable disparate impact, which Microsoft's responsible AI framework explicitly identifies as a fairness violation.
✗InclusivenessWrong answer — click to see why▾
Why this is wrong here
Inclusiveness focuses on designing systems that are accessible to people of all abilities and backgrounds, but the core issue here is unequal performance across demographic groups, which directly violates the Fairness principle.
★ When this WOULD be the correct answer
Inclusiveness would be correct if the question described a system that fails to accommodate users with disabilities (e.g., no support for screen readers) or excludes certain user groups from the design process, rather than having disparate accuracy across ethnic groups.
Why candidates choose this
Candidates may confuse 'inclusiveness' with 'fairness' because both relate to equity, but inclusiveness is about ensuring broad participation and accessibility, not about equal performance across groups.
✗TransparencyWrong answer — click to see why▾
Why this is wrong here
The decision to continue using the system despite known accuracy disparities violates fairness, not transparency. Transparency concerns openness about system behavior, but the issue here is unequal performance across groups, which is a fairness problem.
★ When this WOULD be the correct answer
Transparency would be correct if the question described a system that hides its decision-making process, such as a black-box model used for loan approvals without explaining why a loan was denied, and the organization fails to provide any documentation or reasoning.
Why candidates choose this
Candidates may confuse transparency with fairness because both involve ethical concerns. They might think that disclosing the accuracy disparity would satisfy transparency, but the core violation is the unfair impact, not the lack of disclosure.
✗AccountabilityWrong answer — click to see why▾
Why this is wrong here
Accountability refers to the responsibility of those creating and deploying AI systems for their outcomes. The question focuses on disparate impact on a minority group, which is a fairness issue, not a lack of accountability.
★ When this WOULD be the correct answer
A company deploys an AI system that makes hiring decisions, but there is no clear process for auditing the system's decisions or addressing errors. The company cannot explain who is responsible for the system's outcomes. This would violate the accountability principle.
Why candidates choose this
Candidates may confuse accountability with fairness, thinking that continuing to use a biased system implies a lack of responsibility, but the core issue is the unfair treatment of a specific group.
Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Go deeper
Related to this question
Learn chapter
Responsible AI Principles
Key term
Bias
Bias in AI is a systematic error in data or algorithms that leads to unfair or inaccurate outcomes, often reflecting real-world prejudices.
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
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