Struggling with Responsible AI principles for the AI-102 exam? Learn how transparency and fairness are crucial for monitoring AI customer service systems. Get the answer and boost your Azure AI Engineer skills!
Table of Contents
Question
The Xerigon Corporation is developing an AI customer service system that will process multimedia and emails from existing and prospective customers.
You plan to monitor the system to provide the following requirements:
- A particular region does not dominate results.
- Results would be observed by auditors and be reproduced.
Which of the following responsible AI principles meet the monitoring requirements? Each correct answer presents part of the solution. (Choose two.)
A. Transparency
B. Fairness
C. Accountability
D. Privacy and Security
E. Reliability and Safety
Answer
A. Transparency
B. Fairness
Explanation
Your AI solution directly addresses the challenge of biased decision-making based on more input from a specific location or region, which falls under the Fairness principle of responsible AI. This principle emphasizes that AI systems treats everyone equally and avoids discriminatory outcomes based on certain characteristics, such as gender, race, sexual orientation, or religion. The Fairness principle can help avoid regional bias.
Transparency is important for understanding how AI systems work. Transparency involves making AI systems understandable and providing insights into how they work. This principle can show auditors how data and algorithms were used on the model, what logic was applied to the input, what assets were included in the model, and how the final model was generated. Using the Transparency principle, auditors can observe the logic and reproduce the results.
Reliability and Safety is crucial for ensuring proper system functioning. However, it does not directly address bias in decision-making nor allow the results to be seen.
Accountability involves holding individuals responsible for AI systems and ensuring that ethical and legal standards are met. While important, it does not directly address the avoidance of biased decision making nor allow the results to be seen.
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.
Microsoft Azure AI Engineer Associate AI-102 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Engineer Associate AI-102 exam and earn Microsoft Azure AI Engineer Associate AI-102 certification.