What are some examples of explainable AI?
Explainable AI (XAI) refers to methods and techniques that make AI decisions understandable to humans. Examples include LIME, SHAP, and decision trees.
Can you explain how LIME works?
LIME, or Local Interpretable Model-agnostic Explanations, works by approximating the AI model locally with an interpretable model to explain individual predictions.
What are the benefits of using explainable AI?
The benefits include increased trust in AI systems, better compliance with regulations, and improved model performance through insights gained from explanations.
Are there any real-world applications of explainable AI?
Yes, explainable AI is used in healthcare for diagnosis, finance for credit scoring, and in autonomous vehicles for decision-making transparency.
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