What are some techniques for explainable AI?

What are some techniques for explainable AI?

Explainable AI (XAI) techniques help make AI decisions understandable. Some common techniques include LIME, SHAP, and decision trees.

Can you explain LIME and how it works?

LIME, or Local Interpretable Model-agnostic Explanations, approximates complex models with simpler ones locally to explain predictions.

What is SHAP and how does it differ from LIME?

SHAP, or SHapley Additive exPlanations, uses game theory to assign each feature an importance value for a prediction, providing a unified measure.

Are there any other techniques I should know about?

Yes, other techniques include attention mechanisms in neural networks and rule-based models, which are inherently interpretable.