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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 211220 of 971 papers

TitleStatusHype
Impact of satellites streaks for observational astronomy: a study on data captured during one year from Luxembourg Greater RegionCode0
Explainable Image Recognition via Enhanced Slot-attention Based Classifier0
From Data to Commonsense Reasoning: The Use of Large Language Models for Explainable AI0
How Reliable and Stable are Explanations of XAI Methods?0
A Survey of Accessible Explainable Artificial Intelligence Research0
Explainability of Machine Learning Models under Missing DataCode0
ShapG: new feature importance method based on the Shapley valueCode0
FreqRISE: Explaining time series using frequency maskingCode0
MiSuRe is all you need to explain your image segmentation0
GFM4MPM: Towards Geospatial Foundation Models for Mineral Prospectivity Mapping0
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