Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A healthcare organization uses an AI system to predict patient readmission risk. The model was trained on data from a single hospital with a predominantly elderly population. When deployed to a different hospital with a younger demographic, the model's accuracy drops significantly. Which responsible AI principle is most directly violated?
⚠ Common exam trap
Test-takers frequently confuse a model's failure to generalize (reliability) with fairness, because candidates may incorrectly assume that any performance disparity across demographic groups automatically constitutes a fairness violation.
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
✓
Reliability and safety
The model's accuracy drop when applied to a different demographic indicates a failure in reliability and safety. The model was trained on a non-representative dataset (elderly patients) and does not generalize to younger populations, violating the principle that AI systems must perform consistently and safely across intended deployment contexts.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Transparency
Why it's wrong here
Transparency is about making AI systems interpretable and providing clear explanations for their decisions. In this scenario, the core problem is not a lack of explainability but a performance degradation when the model is applied to a new demographic. A completely transparent model could still fail identically, so the most direct violation is the principle of reliability and safety, not transparency.
- ✗
Fairness
Why it's wrong here
Fairness concerns bias against groups. While the model may perform worse for younger patients, the core issue is that it was not designed to generalize across demographics, but the most direct violation is reliability, because the system fails to work safely in a new context.
- ✓
Reliability and safety
Why this is correct
This principle requires AI systems to perform as intended across a range of conditions and to be robust to changes in data distribution. The model's failure to generalize to a different demographic violates this principle.
- ✗
Privacy and security
Why it's wrong here
Privacy and security are concerned with protecting patient data from unauthorized access, breaches, and misuse. Here, there is no indication of any data exposure, insecure storage, or improper handling of personal health information. The model's failure to generalize across age groups is a robustness issue, not a data protection issue, making the reliability and safety principle more applicable.
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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
Reliability and safety
Reliability and safety in IT means that systems consistently perform their intended functions without failure and that they operate without causing harm to people, data, or the environment.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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