Learn how the principle of Reliability and Safety ensures AI systems for medical diagnoses are designed to minimize errors and protect patient safety.
Table of Contents
Question
You want to design AI systems for medical diagnoses, ensuring that they pose minimal risk of errors or misdiagnosis to patient safety. Which principle of responsible AI does this approach cover?
A. Accountability
B. Inclusiveness
C. Transparency
D. Reliability and Safety
Answer
D. Reliability and Safety
Explanation
Minimizing risk covers the principle of Reliability and Safety. This principle emphasizes that AI systems, particularly those in critical areas such as healthcare, must operate reliably and pose minimal risk. This directly aligns with the need to ensure accurate and safe diagnoses in medical applications.
The approach mentioned in the scenario does not cover Transparency. While transparency is important in understanding how AI systems work, it does not directly address the issue of minimizing risk in medical diagnoses.
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 minimizing risk in this context.
The approach mentioned in the scenario does not cover Inclusiveness. While important for ensuring that everyone has access to healthcare, it does not directly address the technical aspect of minimizing risk in the AI system itself.
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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