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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 501510 of 971 papers

TitleStatusHype
When concept-based XAI is imprecise: Do people distinguish between generalisations and misrepresentations?0
Where and When: Space-Time Attention for Audio-Visual Explanations0
Which LIME should I trust? Concepts, Challenges, and Solutions0
Understanding User Preferences in Explainable Artificial Intelligence: A Survey and a Mapping Function Proposal0
Widespread Increases in Future Wildfire Risk to Global Forest Carbon Offset Projects Revealed by Explainable AI0
XAI and Android Malware Models0
XAI Benchmark for Visual Explanation0
XAI-CF -- Examining the Role of Explainable Artificial Intelligence in Cyber Forensics0
XAI for All: Can Large Language Models Simplify Explainable AI?0
XAI-FUNGI: Dataset resulting from the user study on comprehensibility of explainable AI algorithms0
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