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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 4150 of 971 papers

TitleStatusHype
Calibrated Explanations: with Uncertainty Information and CounterfactualsCode1
An XAI framework for robust and transparent data-driven wind turbine power curve modelsCode1
Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep ModelsCode1
Towards Trust of Explainable AI in Thyroid Nodule DiagnosisCode1
Finding Alignments Between Interpretable Causal Variables and Distributed Neural RepresentationsCode1
Explainable AI for Bioinformatics: Methods, Tools, and ApplicationsCode1
Causality-Aware Local Interpretable Model-Agnostic ExplanationsCode1
MEGAN: Multi-Explanation Graph Attention NetworkCode1
XAI for transparent wind turbine power curve modelsCode1
Why Should I Choose You? AutoXAI: A Framework for Selecting and Tuning eXplainable AI SolutionsCode1
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