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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 731740 of 971 papers

TitleStatusHype
Integrating Evidence into the Design of XAI and AI-based Decision Support Systems: A Means-End Framework for End-users in Construction0
Integrating Explainable AI for Effective Malware Detection in Encrypted Network Traffic0
Integrating Intrinsic and Extrinsic Explainability: The Relevance of Understanding Neural Networks for Human-Robot Interaction0
Integrating Prior Knowledge in Post-hoc Explanations0
Explainable AI Integrated Feature Selection for Landslide Susceptibility Mapping using TreeSHAP0
Intelligent Decision Assistance Versus Automated Decision-Making: Enhancing Knowledge Work Through Explainable Artificial Intelligence0
Intensional Artificial Intelligence: From Symbol Emergence to Explainable and Empathetic AI0
Interactive dense pixel visualizations for time series and model attribution explanations0
Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review0
Interpretability and Explainability: A Machine Learning Zoo Mini-tour0
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