What are some examples of interpretability in AI?

What are some examples of interpretability in AI?

Interpretability in AI refers to how well humans can understand the decisions made by AI systems. Examples include decision trees, which are easy to visualize, and LIME, which explains predictions by approximating the model locally.

Can you explain LIME in more detail?

LIME, or Local Interpretable Model-agnostic Explanations, works by perturbing the input data and observing the changes in predictions. It creates a simpler model that approximates the complex model's behavior in the vicinity of the instance being explained.

What about SHAP? How does it compare to LIME?

SHAP, or SHapley Additive exPlanations, provides a unified measure of feature importance based on cooperative game theory. Unlike LIME, which focuses on local explanations, SHAP offers both local and global interpretability, making it more comprehensive.

Are there any tools available for implementing these methods?

Yes, there are several libraries available for implementing LIME and SHAP, such as the 'lime' library for Python and 'shap' library, which provide easy-to-use functions for generating explanations.