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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 541550 of 971 papers

TitleStatusHype
Explainable AI: current status and future directions0
Explainable AI does not provide the explanations end-users are asking for0
Explainable AI-Driven Neural Activity Analysis in Parkinsonian Rats under Electrical Stimulation0
Explainable AI for Earth Observation: Current Methods, Open Challenges, and Opportunities0
Explainable AI for Embedded Systems Design: A Case Study of Static Redundant NVM Memory Write Prediction0
Explainable AI for tailored electricity consumption feedback -- an experimental evaluation of visualizations0
Explainable AI for Time Series via Virtual Inspection Layers0
Explainable AI for tool wear prediction in turning0
Explainable AI-Guided Efficient Approximate DNN Generation for Multi-Pod Systolic Arrays0
Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method0
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