Microsoft Azure AI Fundamentals (AI-900)Describe AI workloads and considerationsHard
A healthcare provider is developing an AI system to assist doctors in diagnosing rare diseases based on patient symptoms, medical history, and lab results. The system is highly complex, using deep learning models that produce accurate predictions but are difficult to interpret. The provider wants to ensure that doctors can understand *why* the AI made a particular diagnostic suggestion, especially given the critical nature of health decisions. Which Responsible AI guiding principle is most relevant to this concern?
- AReliability and Safety
- BPrivacy and Security
- CTransparency
- DFairness
Show answer & explanationAnswer & explanation
Correct answer: C. Transparency
Transparency in Responsible AI emphasizes that AI systems should be understandable, allowing users to comprehend how they work and the rationale behind their decisions. This is crucial for doctors to trust and validate diagnostic suggestions, especially from 'black box' deep learning models.
Why the other options are wrong
- A. Reliability and Safety focuses on consistent performance and harm prevention, not explaining *how* decisions are made.
- B. Privacy and Security deals with data protection, unrelated to model interpretability.
- D. Fairness ensures equitable treatment, not the interpretability of decisions.
Responsible AI: Transparency
AI systems should be understandable, allowing users to comprehend how they work, their purpose, and the rationale behind their decisions.
- Promotes trust and accountability.
- Crucial for critical applications like healthcare and finance.
- Achieved through explainable AI (XAI) techniques.
Memory trick: Transparent AI, no hidden 'why'.