Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A bank deploys an AI system to approve personal loan applications. After six months, an audit reveals that applicants from certain postal codes receive significantly lower approval rates than applicants from other postal codes, even when their income and credit scores are comparable. Which Microsoft responsible AI principle is most directly violated by this outcome?
⚠ Common exam trap
Microsoft often tests the distinction between Fairness (outcome-based equity) and Transparency (explainability), so candidates mistakenly choose Transparency when they see an audit revealing bias, thinking the issue is that the model's reasoning isn't clear.
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 AI system's approval decisions produce systematically different outcomes for applicants from different postal codes despite comparable income and credit scores, which directly violates the Fairness principle. Fairness requires that AI systems treat all individuals and groups equitably, avoiding discrimination based on sensitive attributes like location. The audit evidence shows the model has learned spurious correlations between postal code and loan risk, leading to biased approval rates.
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
The fairness principle requires AI systems to avoid discriminatory outcomes across protected or sensitive attributes. Here, the model systematically approves loans at different rates based on postal code, which often acts as a proxy for race, income, or ethnicity; this disparate impact is a fairness violation even if the decision rules seem neutral.
- ✗
Transparency
Why it's wrong here
Transparency deals with whether users and stakeholders know they are interacting with AI and can understand how a particular decision was reached; the complaint in the scenario is not about missing explanations or hidden automation, but about unequal approval outcomes by postal code. A transparent, well-documented model could still produce this biased result, so the core issue remains fairness.
- ✗
Inclusiveness
Why it's wrong here
Inclusiveness centers on designing AI to accommodate diverse human experiences, such as people with disabilities, language differences, or varying accessibility needs, not on equalizing outcomes across geographic regions. The postal-code disparity is a matter of equitable treatment and bias mitigation, whereas inclusiveness would target removing barriers to access or usability for specific groups.
- ✗
Reliability and safety
Why it's wrong here
Reliability and safety concern whether an AI performs consistently, within specifications, and without dangerous failures; a biased loan model can be highly reliable in the sense that it consistently reproduces historical lending patterns and still be unfair. Because the violation emerges from biased training data or proxy features rather than a technical malfunction, it does not fall under this principle.
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Responsible AI Principles
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
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.
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