AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A hospital deploys an AI system to recommend treatment plans for patients. After deployment, the system is found to have significantly lower accuracy for patients from certain racial and ethnic groups because historical medical data for those groups is sparse. Which Microsoft responsible AI principle should the hospital prioritize to address this issue?
⚠ Common exam trap
Candidates often confuse fairness with inclusiveness, thinking that including more diverse data is the same as ensuring equitable outcomes, but fairness specifically addresses the algorithmic bias that arises from data imbalances.
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
Fairness. The AI system's lower accuracy for certain racial and ethnic groups is a direct fairness issue, as it produces biased outcomes due to sparse historical data. Microsoft's Fairness principle requires AI systems to treat all groups equitably and mitigate disparities in performance, which is the core problem here.
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 aims to empower everyone and ensure AI benefits all people, but the core problem is unequal performance, which is a fairness concern. Inclusiveness is broader and does not specifically focus on eliminating outcome disparities.
- ✓
Fairness
Why this is correct
Fairness is the correct principle because the core failure is a measurable disparity in recommendation accuracy between demographic groups. In clinical AI, fairness requires that model performance—such as sensitivity, specificity, and positive predictive value—be comparable across all patient populations. Addressing this can involve reweighting training samples, collecting more representative data, or applying algorithmic mitigation techniques like equalized odds constraints. This directly targets the biased outcome, unlike broader principles that only govern process or communication.
- ✗
Transparency
Why it's wrong here
Transparency focuses on making the AI system's decision process interpretable, for example through SHAP values, feature importance, or post-hoc explainers. While these tools can reveal that age or ethnicity influences predictions, they do not modify the model or data to remove the performance gap. In a treatment-planning context, a transparent but biased model still produces suboptimal recommendations for certain patients, so transparency alone is insufficient to satisfy the goal of equitable clinical outcomes.
- ✗
Accountability
Why it's wrong here
Accountability concerns the assignment of responsibility, including audit trails, human-in-the-loop oversight, and clear ownership for AI-driven decisions. In a hospital, this might mean a clinician is ultimately responsible for the recommended treatment, but that does not correct the model's unequal accuracy across demographic subgroups. Accountability can trigger investigations or remediation, but the principle itself is a governance mechanism, not a technical fix for bias; without fairness-specific tuning, the disparity remains.
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.
About these practice questions
One of 985 original AI-900 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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.