This repository presents a comprehensive research paper exploring the role of Explainable Artificial Intelligence (XAI) in modern Machine Learning. It aims to shed light on the interpretability of 'black-box' models like Neural Networks, Explainable AI and highlights the need for transparent, human-understandable ML systems.
knowledge-graph neural lime black-box-model xai interpretable-machine-learning shap counterfactual-explanations local-explanations post-hoc-analysis model-agnostic-explanations post-hoc-explanation global-explanation regulatory-compliance human-ai-interaction model-specific-explanations human-ai-collaboration transparency-trade-off performance-trade-off inherently-interpretable-models
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