What are some examples of AI interpretability?
AI interpretability refers to methods that make AI decisions understandable. Examples include feature importance, LIME, and SHAP.
Can you explain LIME and how it works?
LIME, or Local Interpretable Model-agnostic Explanations, explains individual predictions by approximating the model locally with an interpretable one.
What about SHAP? How is it different from LIME?
SHAP, or SHapley Additive exPlanations, provides consistent feature importance values based on cooperative game theory, offering a unified measure.
Are there any real-world applications of these methods?
Yes, they are used in healthcare for model transparency, finance for risk assessment, and in legal settings to explain decisions.
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