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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 441450 of 971 papers

TitleStatusHype
eXplainable Artificial Intelligence (XAI) in aging clock models0
TbExplain: A Text-based Explanation Method for Scene Classification Models with the Statistical Prediction Correction0
What's meant by explainable model: A Scoping Review0
Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing0
Measuring Perceived Trust in XAI-Assisted Decision-Making by Eliciting a Mental Model0
Gastrointestinal Disease Classification through Explainable and Cost-Sensitive Deep Neural Networks with Supervised Contrastive LearningCode0
Visual Explanations with Attributions and Counterfactuals on Time Series Classification0
Explainable Artificial Intelligence driven mask design for self-supervised seismic denoising0
On the Connection between Game-Theoretic Feature Attributions and Counterfactual Explanations0
A Deep Dive into Perturbations as Evaluation Technique for Time Series XAICode0
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