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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 110 of 971 papers

TitleStatusHype
Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Survey0
From Motion to Meaning: Biomechanics-Informed Neural Network for Explainable Cardiovascular Disease Identification0
Towards Transparent AI: A Survey on Explainable Large Language Models0
IXAII: An Interactive Explainable Artificial Intelligence Interface for Decision Support Systems0
Communicating Smartly in the Molecular Domain: Neural Networks in the Internet of Bio-Nano ThingsCode0
Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach0
Toward the Explainability of Protein Language Models for Sequence Design0
When concept-based XAI is imprecise: Do people distinguish between generalisations and misrepresentations?0
A Systematic Review of User-Centred Evaluation of Explainable AI in Healthcare0
Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence0
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