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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 7180 of 971 papers

TitleStatusHype
Toward Improving Confidence in Autonomous Vehicle Software: A Study on Traffic Sign Recognition SystemsCode1
Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property PredictionCode1
Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAyCode1
Entropy-based Logic Explanations of Neural NetworksCode1
To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methodsCode1
This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep NetworksCode1
Visualizing Adapted Knowledge in Domain TransferCode1
Text Guide: Improving the quality of long text classification by a text selection method based on feature importanceCode1
Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark DatasetCode1
TorchPRISM: Principal Image Sections Mapping, a novel method for Convolutional Neural Network features visualizationCode1
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