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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 376–400 of 971 papers

TitleStatusHype
Explainable artificial intelligence for Healthcare applications using Random Forest Classifier with LIME and SHAP—0
Explainable AI for Earth Observation: Current Methods, Open Challenges, and Opportunities—0
Explainable artificial intelligence model for identifying Market Value in Professional Soccer Players—0
Extracting human interpretable structure-property relationships in chemistry using XAI and large language modelsCode1
Explainable Authorship Identification in Cultural Heritage Applications: Analysis of a New PerspectiveCode0
Assessing Fidelity in XAI post-hoc techniques: A Comparative Study with Ground Truth Explanations DatasetsCode0
Notion of Explainable Artificial Intelligence -- An Empirical Investigation from A Users Perspective—0
Transferability and explainability of deep learning emulators for regional climate model projections: Perspectives for future applications—0
Learning impartial policies for sequential counterfactual explanations using Deep Reinforcement Learning—0
Medical Image Denosing via Explainable AI Feature Preserving Loss—0
Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions—0
Popularity, face and voice: Predicting and interpreting livestreamers' retail performance using machine learning techniques—0
Using Slisemap to interpret physical dataCode1
Local Universal Explainer (LUX) -- a rule-based explainer with factual, counterfactual and visual explanationsCode1
Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification—0
Mobile Traffic Prediction at the Edge Through Distributed and Deep Transfer Learning—0
XAI Benchmark for Visual Explanation—0
The Thousand Faces of Explainable AI Along the Machine Learning Life Cycle: Industrial Reality and Current State of Research—0
In-Context Explainers: Harnessing LLMs for Explaining Black Box ModelsCode1
EAG-RS: A Novel Explainability-guided ROI-Selection Framework for ASD Diagnosis via Inter-regional Relation LearningCode0
aSAGA: Automatic Sleep Analysis with Gray Areas—0
Trainable Noise Model as an XAI evaluation method: application on Sobol for remote sensing image segmentationCode1
Extending CAM-based XAI methods for Remote Sensing Imagery SegmentationCode1
Refutation of Shapley Values for XAI -- Additional Evidence—0
Sampling - Variational Auto Encoder - Ensemble: In the Quest of Explainable Artificial Intelligence—0
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