Discover how quantifying data feature influences can ensure fairness in AI systems. Learn its role in responsible AI development and Microsoft Azure AI-900 exam preparation.
Table of Contents
Question
When developing solutions in line with the principles of responsible AI, which practice can ensure the principle of Fairness?
A. Ensuring privacy and security of sensitive data in the system
B. Quantifying the levels to which data features influence predictions
C. Creating rigorous testing and deployment management systems
D. Creating the solution inside a governance framework
Answer
B. Quantifying the levels to which data features influence predictions
Explanation
Quantifying the levels to which data features influence predictions is used to ensure the principle of Fairness in developing responsible AI solutions. This practice allows developers to identify potential biases in the data or model by analyzing how different features (e.g., gender, ethnicity) contribute to the AI’s decision-making process. By identifying such biases, developers can take steps to mitigate them and ensure fairer outcomes.
Creating rigorous testing and deployment management systems is related to the principle of Reliability and Safety, not Fairness. While crucial for responsible AI, rigorous testing and deployment management do not directly address identifying or mitigating bias within the model itself. They ensure that the system functions as intended but do not necessarily guarantee fair outcomes.
Ensuring privacy and security of sensitive data in the system is related to the principle of Privacy and Security, not Fairness. While essential for responsible AI, data privacy and security are not directly related to identifying or mitigating bias in the model. They protect data privacy but do not address potential biases arising from the data or model itself.
Creating the solution inside a governance framework is related to the principle of Accountability, not Fairness. While governance frameworks can help enforce ethical guidelines, they do not directly provide specific tools for quantifying and mitigating bias within the model.
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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