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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 961970 of 971 papers

TitleStatusHype
Proceedings of the 1st Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI 2019)0
Some Insights Towards a Unified Semantic Representation of Explanation for eXplainable Artificial Intelligence0
Representation, Justification and Explanation in a Value Driven Agent: An Argumentation-Based Approach0
Explainable artificial intelligence (XAI), the goodness criteria and the grasp-ability test0
Explainable Security0
Towards a Grounded Dialog Model for Explainable Artificial Intelligence0
SoPa: Bridging CNNs, RNNs, and Weighted Finite-State MachinesCode0
Improved Explainability of Capsule Networks: Relevance Path by Agreement0
Visual Interpretability for Deep Learning: a SurveyCode0
Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models0
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