What is the importance of SHAP in AI?

What is the importance of SHAP in AI?

SHAP, or SHapley Additive exPlanations, is crucial for interpreting AI models. It helps in understanding feature contributions.

Can you explain how SHAP values are calculated?

SHAP values are calculated based on cooperative game theory, assigning each feature a value based on its contribution to the prediction.

Why is interpretability important in AI?

Interpretability is vital for trust, accountability, and compliance in AI systems, especially in critical applications like healthcare.

Are there any limitations to using SHAP?

Yes, SHAP can be computationally intensive, especially with large datasets, and may not always provide clear insights for complex models.

How does SHAP compare to other interpretability methods?

SHAP is often preferred for its consistency and theoretical foundation, but other methods like LIME also have their advantages depending on the context.