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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 551560 of 971 papers

TitleStatusHype
SketchXAI: A First Look at Explainability for Human Sketches0
SolderNet: Towards Trustworthy Visual Inspection of Solder Joints in Electronics Manufacturing Using Explainable Artificial Intelligence0
Some Insights Towards a Unified Semantic Representation of Explanation for eXplainable Artificial Intelligence0
Squashing activation functions in benchmark tests: towards eXplainable Artificial Intelligence using continuous-valued logic0
Stacked ensemble\-based mutagenicity prediction model using multiple modalities with graph attention network0
STARdom: an architecture for trusted and secure human-centered manufacturing systems0
Statistical tuning of artificial neural network0
Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability0
Strategies to exploit XAI to improve classification systems0
Survey of XAI in digital pathology0
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