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Download PDFOpen PDF in browserExplainable Artificial Intelligence: an Overview on Hybrid ModelsEasyChair Preprint 1473211 pages•Date: September 6, 2024AbstractThe increasing integration of Artificial Intelligence (AI) in various critical areas highlights the need to both achieve accuracy in predictions and understand the logic behind them for proper decision making. Explainable Artificial Intelligence (XAI) addresses this challenge, balancing the complexity of models with the necessary transparency and interpretability. Hybrid models, by integrating the accuracy of black-box models with the transparency of interpretable ones, represent a promising avenue in the move towards more understandable, accurate and reliable systems in AI, encouraging their safe, ethical and responsible adoption in diverse real-world applications. This paper provides an exploration of hybrid models in XAI, elaborating on key concepts and offering a classification based on interpretability. In addition to describing the construction of these models, it reviews advances in the literature and identifies future directions. Keyphrases: Explainable Artificial Intelligence, XAI, black-box models, hybrid models, interpretability Download PDFOpen PDF in browser |
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