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Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions

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June 1, 2024
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Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions

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A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME

eXplainable artificial intelligence (XAI) methods have emerged to convert the black box of machine learning (ML) models into a more digestible form. These methods help to communicate how the model ...

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Towards Transparent Healthcare: Advancing Local Explanation Methods in Explainable Artificial Intelligence

This paper focuses on the use of local Explainable Artificial Intelligence (XAI) methods, particularly the Local Rule-Based Explanations (LORE) technique, within healthcare and medical settings. It...

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Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer’s disease detection

AbstractExplainable artificial intelligence (XAI) has gained much interest in recent years for its ability to explain the complex decision-making process of machine learning (ML) and deep learning ...

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Artificial Intelligence for Social Good: Transforming the Lives of Tribal Women Entrepreneurs

The convergence of Artificial Intelligence for Social Good (AI4SG) and the marginalized economic sectors is a boundary in the inclusive technological development. The study explores the potential o...

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What other academic literature is closely related to 'Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions'?

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