Discover the critical requirements for ethical AI decision-making tested in the Microsoft Azure AI-900 certification, including avoiding bias based on gender and other sensitive factors.
Table of Contents
Question
Which of the following is a requirement for ensuring fairness in an AI solution?
A. Careful handling of sensitive personal information
B. Deployment processes for ensuring expected functionality
C. Avoiding decision-making based on factors such as gender
D. Taking responsibility for ensuring that the solution follows legal requirements
Answer
C. Avoiding decision-making based on factors such as gender
Explanation
Avoiding decision making based on factors such as gender is a requirement for ensuring fairness in an AI solution. AI systems should treat everyone equally and avoid biased decision-making based on factors such as gender and ethnicity. Azure Machine Learning tools help identify and mitigate potential biases in models. Microsoft retired facial recognition features that could infer emotional states due to potential misuse and discrimination.
Deployment processes for ensuring expected functionality is not related to fairness. This is a requirement for reliability and safety. AI systems, particularly those in critical areas such as autonomous vehicles and medical diagnoses, must operate reliably and pose minimal risk. Rigorous testing and deployment management processes are crucial to ensuring expected functionality.
Careful handling of sensitive personal information is not related to fairness. This is a requirement for privacy and security. AI systems must respect data privacy and security, especially when handling sensitive personal information used in training models.
Taking responsibility for ensuring that the solution follows legal requirements is not related to fairness. This is a requirement for accountability. Well-defined governance frameworks are crucial for achieving responsible AI development.
The six key principles of responsible AI include:
- Fairness: AI systems should treat all people fairly, avoiding biases based on factors such as gender and ethnicity.
- Reliability and Safety: AI systems should perform reliably and safely, with rigorous testing and deployment management to ensure expected functionality and minimize risks.
- Privacy and Security: AI systems should be secure and respect privacy, considering the privacy implications of the data used and decisions made by the system.
- Inclusiveness: AI systems should empower and engage everyone, bringing benefits to all parts of society without discrimination.
- Transparency: AI systems should be understandable, with users fully aware of the system’s purpose, functioning, and limitations.
- Accountability: People should be accountable for AI systems, working within a framework of governance and organizational principles to meet ethical and legal standards.
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