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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 591600 of 971 papers

TitleStatusHype
Towards Benchmarking Explainable Artificial Intelligence Methods0
When Stability meets Sufficiency: Informative Explanations that do not Overwhelm0
Towards explainable meta-learning0
Towards Explainable Artificial Intelligence0
Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective0
Towards Harnessing Natural Language Generation to Explain Black-box Models0
Towards Human Cognition Level-based Experiment Design for Counterfactual Explanations (XAI)0
Towards Interpretability in Audio and Visual Affective Machine Learning: A Review0
Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach0
Towards Quantification of Explainability in Explainable Artificial Intelligence Methods0
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