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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 921930 of 971 papers

TitleStatusHype
The Grammar of Interactive Explanatory Model AnalysisCode1
Explainable Goal-Driven Agents and Robots -- A Comprehensive Review0
Foundations of Explainable Knowledge-Enabled Systems0
Learning to Structure an Image with Few ColorsCode1
Directions for Explainable Knowledge-Enabled Systems0
Vector symbolic architectures for context-free grammars0
EXPLAIN-IT: Towards Explainable AI for Unsupervised Network Traffic Analysis0
The Pragmatic Turn in Explainable Artificial Intelligence (XAI)0
Towards explainable meta-learning0
What Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model ConversationsCode0
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