Learn how inclusiveness, a key principle of responsible AI, ensures accessibility for diverse users in AI systems. Essential for the Microsoft Azure AI-900 exam.
Table of Contents
Question
You are designing AI solutions for a sports company and need to ensure that the system is accessible to people of varying physical ability. Which principle of responsible AI does this approach cover?
A. Accountability
B. Transparency
C. Inclusiveness
D. Fairness
Answer
C. Inclusiveness
Explanation
The approach mentioned in the scenario covers Inclusiveness. This principle emphasizes that AI systems should empower and engage everyone, regardless of factors like physical ability, gender, sexual orientation, and ethnicity. Designing an accessible system ensures that individuals with varying physical abilities can interact with and benefit from your AI solution in the sports domain.
The approach mentioned in the scenario does not cover Transparency. While transparency is important for understanding how the system works, it does not directly address the issue of accessibility for users with diverse physical abilities.
The approach mentioned in the scenario does not cover Accountability. This principle holds developers responsible for ethical implications, but it is not the specific focus of designing an accessible system in this context.
The approach mentioned in the scenario does not cover Fairness. While fairness is crucial in AI development, it does not directly target accessibility, which falls under the broader concept of inclusivity.
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 Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.