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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 826–850 of 971 papers

TitleStatusHype
Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain—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
TrustyAI Explainability ToolkitCode0
Towards Rigorous Interpretations: a Formalisation of Feature AttributionCode0
Exploiting Explanations for Model Inversion Attacks—0
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
DA-DGCEx: Ensuring Validity of Deep Guided Counterfactual Explanations With Distribution-Aware Autoencoder Loss—0
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
A Novel Approach for Semiconductor Etching Process with Inductive Biases—0
Towards a Rigorous Evaluation of Explainability for Multivariate Time Series—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
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
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