Learn how responsible AI principles like privacy and security are transforming healthcare. Explore strategies to protect patient data while ensuring ethical compliance in AI-driven medical solutions.
Table of Contents
Question
You want to design an AI solution for a healthcare organization that ensures that protected health information (PHI) for users is handled appropriately. Which principle of responsible AI does this approach cover?
A. Transparency
B. Reliability and Safety
C. Privacy and Security
D. Accountability
Answer
C. Privacy and Security
Explanation
The Privacy and Security principle emphasizes that AI systems must respect data privacy and security, especially when handling sensitive personal information such as protected health information (PHI). This directly aligns with the need to protect patient data confidentiality and ensure its secure storage and processing within the AI system.
The mentioned approach does not cover Transparency. While transparency is important in understanding how the AI system works, it does not directly address the specific issue of data privacy and security.
The mentioned approach does not cover Accountability. This principle holds developers responsible for ethical implications, but it is not the specific focus of data privacy and security in this context.
The mentioned approach does not cover Reliability and Safety. While important for ensuring proper system functioning, it does not directly address the issue of data privacy and security.
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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