What are some techniques for AI interpretability?
There are several techniques for AI interpretability, including feature importance, LIME, SHAP, and model distillation.
Can you explain LIME and how it works?
LIME, or Local Interpretable Model-agnostic Explanations, approximates a complex model locally with an interpretable one to explain predictions.
What is SHAP and how does it differ from LIME?
SHAP, or SHapley Additive exPlanations, provides a unified measure of feature importance based on cooperative game theory, offering consistent explanations.
Are there any limitations to these techniques?
Yes, limitations include potential inaccuracies in approximations and the challenge of interpreting complex interactions between features.
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