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Skip to Search Results- 2Natural Language Processing
- 2SHAP
- 1Artificial Intelligence
- 1Causality
- 1Explainable AI
- 1Explainable Artificial Intelligence
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Spring 2015
This work is concerned with the problem of extracting structured information networks from a text corpus. The nodes of the network are recognizable entities, typically people, locations, or organizations, while the edges denote relations among such entities. We use state-of-the-art natural...
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Fall 2023
Explainable artificial intelligence models are becoming increasingly important as restrictions grow for corporate use of blackbox models whose predictions affect people’s lives and yet cannot be interpreted. Black boxes do not convey trust to end-users and are difficult to train and debug for...
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Spring 2023
With machine learning models becoming more complicated and more widely applied to solve real-world challenges, there comes the need to explain their reasoning. In parallel with the advancements of deep learning methods, Explainable AI (XAI) algorithms have been proposed to address the issue of...