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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

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Showing 801–850 of 971 papers

TitleStatusHype
Exploiting auto-encoders and segmentation methods for middle-level explanations of image classification systems—0
Explainable Artificial Intelligence (XAI) for Increasing User Trust in Deep Reinforcement Learning Driven Autonomous Systems—0
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 AI—0
Explainable Activity Recognition for Smart Home Systems—0
Algorithm-Agnostic Explainability for Unsupervised ClusteringCode0
Abstraction, Validation, and Generalization for Explainable Artificial Intelligence—0
A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts—0
Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain—0
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 manufacturing—0
Where and When: Space-Time Attention for Audio-Visual Explanations—0
A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts—0
Regularizing Explanations in Bayesian Convolutional Neural Networks—0
Exploiting Explanations for Model Inversion Attacks—0
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 Cancer—0
Intensional Artificial Intelligence: From Symbol Emergence to Explainable and Empathetic AI—0
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 models—0
Visualizing Adapted Knowledge in Domain TransferCode1
DA-DGCEx: Ensuring Validity of Deep Guided Counterfactual Explanations With Distribution-Aware Autoencoder Loss—0
Text Guide: Improving the quality of long text classification by a text selection method based on feature importanceCode1
Enabling Machine Learning Algorithms for Credit Scoring -- Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models—0
Triplot: model agnostic measures and visualisations for variable importance in predictive models that take into account the hierarchical correlation structureCode0
Towards a Rigorous Evaluation of Explainability for Multivariate Time Series—0
A Novel Approach for Semiconductor Etching Process with Inductive Biases—0
Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing—0
STARdom: an architecture for trusted and secure human-centered manufacturing systems—0
Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey—0
Local Explanations via Necessity and Sufficiency: Unifying Theory and PracticeCode0
Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals—0
Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark DatasetCode1
A Comparative Approach to Explainable Artificial Intelligence Methods in Application to High-Dimensional Electronic Health Records: Examining the Usability of XAI—0
Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications—0
Reasons, Values, Stakeholders: A Philosophical Framework for Explainable Artificial Intelligence—0
An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery—0
What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research—0
VitrAI -- Applying Explainable AI in the Real World—0
Principles of Explanation in Human-AI Systems—0
Mitigating belief projection in explainable artificial intelligence via Bayesian TeachingCode0
Convolutional Neural Network Interpretability with General Pattern Theory—0
Achieving Explainability for Plant Disease Classification with Disentangled Variational Autoencoders—0
Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond—0
A Survey on Understanding, Visualizations, and Explanation of Deep Neural Networks—0
Diagnosis of Acute Poisoning Using Explainable Artificial Intelligence—0
Hierarchical Variational Autoencoder for Visual Counterfactuals—0
Matching Representations of Explainable Artificial Intelligence and Eye Gaze for Human-Machine Interaction—0
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