Explainable AI: Bridging the Gap Between Complex Models and Human Understanding
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Abstract
The increasing adoption of AI in critical domains like healthcare, finance, and legal systems has underscored the need for transparency and interpretability. This paper delves into the concept of Explainable AI (XAI), exploring methods to make complex machine learning models understandable to humans. Techniques such as SHAP, LIME, and model-agnostic approaches are discussed, alongside their application in various industries. The paper highlights the trade-offs between model accuracy and interpretability and emphasizes the importance of user-centric design in XAI systems. Challenges and future prospects for achieving explainability without compromising performance are outlined.
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