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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 3140 of 971 papers

TitleStatusHype
Extending CAM-based XAI methods for Remote Sensing Imagery SegmentationCode1
Insights Into the Inner Workings of Transformer Models for Protein Function PredictionCode1
Calibrated Explanations for RegressionCode1
survex: an R package for explaining machine learning survival modelsCode1
Explaining Black-Box Models through CounterfactualsCode1
FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI MethodsCode1
Model-contrastive explanations through symbolic reasoningCode1
WBCAtt: A White Blood Cell Dataset Annotated with Detailed Morphological AttributesCode1
ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformersCode1
Unlocking the black box of CNNs: Visualising the decision-making process with PRISMCode1
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