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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 626–650 of 971 papers

TitleStatusHype
SolderNet: Towards Trustworthy Visual Inspection of Solder Joints in Electronics Manufacturing Using Explainable Artificial Intelligence—0
Data-Adaptive Discriminative Feature Localization with Statistically Guaranteed InterpretationCode0
Using explainability to design physics-aware CNNs for solving subsurface inverse problems—0
Explainable Artificial Intelligence in Construction: The Content, Context, Process, Outcome Evaluation Framework—0
Explainable Artificial Intelligence: Precepts, Methods, and Opportunities for Research in Construction—0
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in SenegalCode0
REVEL Framework to measure Local Linear Explanations for black-box models: Deep Learning Image Classification case of studyCode0
Motif-guided Time Series Counterfactual Explanations—0
Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions—0
Backtracking Counterfactuals—0
Towards Human Cognition Level-based Experiment Design for Counterfactual Explanations (XAI)—0
Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence—0
A Temporal Type-2 Fuzzy System for Time-dependent Explainable Artificial Intelligence—0
Sustainable Personalisation and Explainability in Dyadic Data Systems—0
Black Box Model Explanations and the Human Interpretability Expectations -- An Analysis in the Context of Homicide PredictionCode0
Explanations Based on Item Response Theory (eXirt): A Model-Specific Method to Explain Tree-Ensemble Model in Trust PerspectiveCode0
Explaining machine learning models for age classification in human gait analysis—0
Explaining automated gender classification of human gait—0
Machine Learning in Transaction Monitoring: The Prospect of xAI—0
InFIP: An Explainable DNN Intellectual Property Protection Method based on Intrinsic Features—0
Neuro-symbolic Explainable Artificial Intelligence Twin for Zero-touch IoE in Wireless Network—0
Toward the application of XAI methods in EEG-based systems—0
T5 for Hate Speech, Augmented Data and EnsembleCode0
Utilizing Explainable AI for improving the Performance of Neural Networks—0
Fault Diagnosis using eXplainable AI: a Transfer Learning-based Approach for Rotating Machinery exploiting Augmented Synthetic Data—0
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