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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 426–450 of 971 papers

TitleStatusHype
Deciphering knee osteoarthritis diagnostic features with explainable artificial intelligence: A systematic review—0
Explainable AI for tool wear prediction in turning—0
BSED: Baseline Shapley-Based Explainable Detector—0
Explaining Black-Box Models through CounterfactualsCode1
DCNFIS: Deep Convolutional Neuro-Fuzzy Inference System—0
FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI MethodsCode1
Predicting and explaining nonlinear material response using deep Physically Guided Neural Networks with Internal Variables—0
Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Model—0
Towards the Visualization of Aggregated Class Activation Maps to Analyse the Global Contribution of Class Features—0
A New Perspective on Evaluation Methods for Explainable Artificial Intelligence (XAI)—0
Revisiting the Performance-Explainability Trade-Off in Explainable Artificial Intelligence (XAI)—0
Do humans and Convolutional Neural Networks attend to similar areas during scene classification: Effects of task and image type—0
Concept backpropagation: An Explainable AI approach for visualising learned concepts in neural network modelsCode0
QAmplifyNet: Pushing the Boundaries of Supply Chain Backorder Prediction Using Interpretable Hybrid Quantum-Classical Neural Network—0
Identifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach—0
eXplainable Artificial Intelligence (XAI) in aging clock models—0
TbExplain: A Text-based Explanation Method for Scene Classification Models with the Statistical Prediction Correction—0
What's meant by explainable model: A Scoping Review—0
Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing—0
Measuring Perceived Trust in XAI-Assisted Decision-Making by Eliciting a Mental Model—0
Gastrointestinal Disease Classification through Explainable and Cost-Sensitive Deep Neural Networks with Supervised Contrastive LearningCode0
Visual Explanations with Attributions and Counterfactuals on Time Series Classification—0
Explainable Artificial Intelligence driven mask design for self-supervised seismic denoising—0
On the Connection between Game-Theoretic Feature Attributions and Counterfactual Explanations—0
A Deep Dive into Perturbations as Evaluation Technique for Time Series XAICode0
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