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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 641650 of 971 papers

TitleStatusHype
VR-LENS: Super Learning-based Cybersickness Detection and Explainable AI-Guided Deployment in Virtual Reality0
Weak Robust Compatibility Between Learning Algorithms and Counterfactual Explanation Generation Algorithms0
WebXAII: an open-source web framework to study human-XAI interaction0
What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks0
What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research0
What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors0
What's meant by explainable model: A Scoping Review0
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
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