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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 921930 of 971 papers

TitleStatusHype
ExplainReduce: Summarising local explanations via proxiesCode0
Addressing the Scarcity of Benchmarks for Graph XAICode0
Why model why? Assessing the strengths and limitations of LIMECode0
Concept backpropagation: An Explainable AI approach for visualising learned concepts in neural network modelsCode0
Do Protein Transformers Have Biological Intelligence?Code0
Communicating Smartly in the Molecular Domain: Neural Networks in the Internet of Bio-Nano ThingsCode0
Analyzing and Improving the Robustness of Tabular Classifiers using Counterfactual ExplanationsCode0
Using Explainable AI and Transfer Learning to understand and predict the maintenance of Atlantic blocking with limited observational dataCode0
Looking into Concept Explanation Methods for Diabetic Retinopathy ClassificationCode0
Explanations for Answer Set ProgrammingCode0
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