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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 671680 of 971 papers

TitleStatusHype
Flood Prediction and Analysis on the Relevance of Features using Explainable Artificial Intelligence0
FNDEX: Fake News and Doxxing Detection with Explainable AI0
Focal Cortical Dysplasia Type II Detection Using Cross Modality Transfer Learning and Grad-CAM in 3D-CNNs for MRI Analysis0
Formal Explanations for Neuro-Symbolic AI0
Forma mentis networks predict creativity ratings of short texts via interpretable artificial intelligence in human and GPT-simulated raters0
Forms of Understanding of XAI-Explanations0
Foundations of Explainable Knowledge-Enabled Systems0
Found in Translation: semantic approaches for enhancing AI interpretability in face verification0
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI0
From Classification to Segmentation with Explainable AI: A Study on Crack Detection and Growth Monitoring0
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