What are the different types of AI explainability?

What are the different types of AI explainability?

AI explainability can be categorized into several types, including model-specific, post-hoc, and global explainability.

Can you explain what model-specific explainability means?

Model-specific explainability refers to techniques that are designed to explain the decisions of a particular model, like decision trees.

What about post-hoc explainability?

Post-hoc explainability involves analyzing a model's outputs after it has made predictions, using methods like LIME or SHAP.

And global explainability?

Global explainability provides insights into the overall behavior of a model across all inputs, helping to understand its general decision-making process.