AWS Certified AI PractitionerResponsible AIMedium
A government agency is using an AI system to process citizen requests for social benefits. They are concerned that the system might make decisions that are difficult to understand or justify to citizens, leading to distrust. To mitigate this, they plan to implement methods that can explain the reasoning behind the AI's recommendations in simple, human-readable language, especially for rejected applications. Which Responsible AI concept are they prioritizing?
- AAlgorithmic bias
- BAI interpretability
- CAI robustness
- DAI governance
Show answer & explanationAnswer & explanation
Correct answer: B. AI interpretability
The goal of explaining AI reasoning in human-readable language, particularly for critical decisions like rejected applications, directly aligns with AI interpretability. This allows for understanding how decisions are made.
Why the other options are wrong
- A. Algorithmic bias is a problem to be addressed, not a concept of making decisions understandable.
- C. AI robustness is about system reliability and security, not explaining decisions.
- D. AI governance is about the overall management and oversight of AI, broader than individual decision explanation.
AI Interpretability
AI interpretability (also known as explainability) is the degree to which a human can understand the cause and effect of an AI system's decisions, especially for complex models.
- Crucial for building trust and accountability.
- Helps justify decisions, particularly in high-stakes domains.
- Can involve techniques like LIME, SHAP, or decision trees.
Memory trick: Explain the AI's thought process.