Explainable AI: Enhancing Transparency in Complex Machine Learning Models
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Abstract
As machine learning models become more complex, the need for explainable AI (XAI) grows. This paper explores techniques for making AI models more transparent and interpretable, focusing on methods such as LIME and SHAP. By applying these techniques to real-world case studies, we demonstrate how XAI can build trust in AI systems and ensure compliance with regulatory standards.
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