AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A company develops an AI system that screens job applications to recommend candidates for interviews. The system consistently recommends male candidates over equally qualified female candidates. Which Microsoft responsible AI principle is most directly violated?
⚠ Common exam trap
Test-takers frequently confuse fairness with inclusiveness, but fairness specifically addresses equitable treatment and non-discrimination in outcomes, whereas inclusiveness is broader about ensuring the system is usable and beneficial to all people.
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 consistent bias toward male candidates over equally qualified female candidates directly violates the fairness principle, which requires AI systems to treat all people equitably and avoid discrimination based on protected attributes like gender. This is a classic case of algorithmic bias, where the model has learned and perpetuated historical or dataset-driven gender disparities in hiring decisions.
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 gender bias observed in the candidate screening system directly violates the fairness principle, which requires AI models to treat individuals equally regardless of protected attributes such as gender. Unlike other principles that focus on technical robustness or data protection, fairness specifically addresses the prevention of discriminatory outcomes in automated decisions. Because the system's predictions are systematically skewed by gender, the core ethical failure is one of fairness.
- ✗
Reliability and safety
Why it's wrong here
Reliability and safety concerns whether an AI system performs consistently, remains robust to unexpected inputs, and avoids causing physical or operational harm. While a biased model could be described as 'unreliable' in everyday language, this principle does not address discriminatory treatment of demographic groups. The scenario reports no system crash, incorrect output due to a malfunction, or safety hazard; the only problem is bias, so this principle is not the governing issue.
- ✗
Privacy and security
Why it's wrong here
Privacy and security focuses on safeguarding personal information during collection, storage, and processing, as well as protecting the AI infrastructure from malicious attacks. The scenario does not describe any data breach, unauthorized access, or misuse of applicants' personal details. Even though the system processes job applications containing sensitive data, the defect is the biased outcome itself, not a compromise of privacy or security, which would involve data exposure or system intrusion.
- ✗
Inclusiveness
Why it's wrong here
Inclusiveness pertains to designing AI systems with diverse user needs, cultural contexts, and accessibility considerations throughout the development lifecycle. A system can be inclusively designed—with representative datasets and accessibility features—yet still produce gender-biased predictions if the underlying algorithm or training labels contain bias. Here, the problem is the discriminatory screening results, which are a fairness violation rather than a failure of inclusive design or a lack of demographic representation in the development team.
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Related to this question
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Responsible AI Principles
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.
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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