100 Facts About Explainable AI (XAI)

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Here’s a list of 100 facts about Explainable AI (XAI):

1. Explainable AI (XAI) aims to make the decision-making process of AI systems understandable to humans.
2. XAI is crucial for building trust in AI systems, especially in sensitive domains like healthcare and finance.
3. Interpretability and transparency are key components of explainability in AI.
4. XAI helps users understand how and why a particular decision was made by an AI model.
5. There are different levels of explainability, ranging from black-box models to fully interpretable models.
6. Local explainability focuses on explaining individual predictions, while global explainability provides insights into the overall behavior of the model.
7. Many AI models, such as deep neural networks, are inherently complex and often considered black boxes.
8. Model-agnostic techniques can be applied to explain predictions from a wide range of AI models.
9. Rule-based models, decision trees, and linear models are inherently more interpretable than complex neural networks.
10. XAI is essential for meeting regulatory requirements in industries like finance and healthcare.
11. Techniques like LIME (Local Interpretable…