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Explainable artificial intelligence

XAI refers to methods and techniques in the application of artificial intelligence (AI) such that the results of the solution can be understood by humans. It contrasts with the concept of the "black box" in machine learning where even its designers cannot explain why an AI arrived at a specific decision. XAI may be an implementation of the social right to explanation. XAI is relevant even if there is no legal right or regulatory requirement—for example, XAI can improve the user experience of a product or service by helping end users trust that the AI is making good decisions. This way the aim of XAI is to explain what has been done, what is done right now, what will be done next and unveil the information the actions are based on. These characteristics make it possible (i) to confirm existing knowledge (ii) to challenge existing knowledge and (iii) to generate new assumptions.

Papers

Showing 701710 of 971 papers

TitleStatusHype
Explainable Machine Learning for Predicting Homicide Clearance in the United States0
Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black BoxCode0
Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization0
Explainability for identification of vulnerable groups in machine learning models0
Deep Learning, Natural Language Processing, and Explainable Artificial Intelligence in the Biomedical Domain0
XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine LearningCode1
Deep Learning Reproducibility and Explainable AI (XAI)0
Guidelines and Evaluation of Clinical Explainable AI in Medical Image AnalysisCode1
XAI in the context of Predictive Process Monitoring: Too much to RevealCode0
Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?Code0
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