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Explainable AI: Techniques and Challenges by marcio.wsouza is a document available to read on EtoBox.

The document discusses Explainable AI (XAI), which aims to enhance transparency and interpretability in machine learning models, particularly as they are increasingly used in critical fields like healthcare and finance. It outlines various techniques for interpreting these models, such as rule-based explanations, feature importance analysis, and surrogate models, while also addressing challenges like the trade-off between interpretability and performance. The document emphasizes the importance of explainabi

Author

marcio.wsouza

Language

EN