AWS Certified AI PractitionerResponsible AIMedium
A government agency is using an AI system to process citizen requests. They are concerned that the AI might make decisions that are difficult to justify or explain to the public, leading to a lack of trust. To mitigate this, they plan to use techniques like LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations). Which Responsible AI concept are these techniques primarily designed to address?
- AInterpretability
- BPrivacy
- CAccountability
- DFairness
Show answer & explanationAnswer & explanation
Correct answer: A. Interpretability
LIME and SHAP are specific techniques used to explain the predictions of complex AI models, making their decisions understandable. This directly addresses the concept of interpretability, which is about understanding how an AI model makes its decisions.
Why the other options are wrong
- B. Privacy deals with protecting sensitive data, not explaining model logic.
- C. Accountability is about assigning responsibility, not making models understandable.
- D. Fairness focuses on equitable outcomes, not directly on explaining individual decisions.
AI Interpretability (Explainability)
AI Interpretability, or explainability, refers to the extent to which a human can understand the cause of a decision made by an AI model. It allows stakeholders to comprehend the rationale behind an AI's output.
- Crucial for building trust and debugging.
- Techniques include LIME, SHAP, and feature importance.
- Helps identify bias and ensure compliance.
Memory trick: Explain Your AI, Build Trust.