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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 801825 of 971 papers

TitleStatusHype
Exploiting auto-encoders and segmentation methods for middle-level explanations of image classification systems0
Explainable Artificial Intelligence (XAI) for Increasing User Trust in Deep Reinforcement Learning Driven Autonomous Systems0
To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methodsCode1
Bounded logit attention: Learning to explain image classifiersCode0
Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI0
Explainable Activity Recognition for Smart Home Systems0
Algorithm-Agnostic Explainability for Unsupervised ClusteringCode0
Abstraction, Validation, and Generalization for Explainable Artificial Intelligence0
A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts0
Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain0
This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep NetworksCode1
XAI-KG: knowledge graph to support XAI and decision-making in manufacturing0
Where and When: Space-Time Attention for Audio-Visual Explanations0
A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts0
Regularizing Explanations in Bayesian Convolutional Neural Networks0
Exploiting Explanations for Model Inversion Attacks0
Towards Rigorous Interpretations: a Formalisation of Feature AttributionCode0
TrustyAI Explainability ToolkitCode0
Explainable Artificial Intelligence Reveals Novel Insight into Tumor Microenvironment Conditions Linked with Better Prognosis in Patients with Breast Cancer0
Intensional Artificial Intelligence: From Symbol Emergence to Explainable and Empathetic AI0
Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental AnalysisCode0
Explainable artificial intelligence for mechanics: physics-informing neural networks for constitutive models0
Visualizing Adapted Knowledge in Domain TransferCode1
DA-DGCEx: Ensuring Validity of Deep Guided Counterfactual Explanations With Distribution-Aware Autoencoder Loss0
Text Guide: Improving the quality of long text classification by a text selection method based on feature importanceCode1
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