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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 676–700 of 971 papers

TitleStatusHype
Forms of Understanding of XAI-Explanations—0
Foundations of Explainable Knowledge-Enabled Systems—0
Found in Translation: semantic approaches for enhancing AI interpretability in face verification—0
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI—0
From Classification to Segmentation with Explainable AI: A Study on Crack Detection and Growth Monitoring—0
From Data to Commonsense Reasoning: The Use of Large Language Models for Explainable AI—0
From Interpretable Filters to Predictions of Convolutional Neural Networks with Explainable Artificial Intelligence—0
From Motion to Meaning: Biomechanics-Informed Neural Network for Explainable Cardiovascular Disease Identification—0
From Pixels to Words: Leveraging Explainability in Face Recognition through Interactive Natural Language Processing—0
From Robustness to Explainability and Back Again—0
From SHAP Scores to Feature Importance Scores—0
From What Ifs to Insights: Counterfactuals in Causal Inference vs. Explainable AI—0
GENEOnet: Statistical analysis supporting explainability and trustworthiness—0
Generating detailed saliency maps using model-agnostic methods—0
Geometric Remove-and-Retrain (GOAR): Coordinate-Invariant eXplainable AI Assessment—0
GFM4MPM: Towards Geospatial Foundation Models for Mineral Prospectivity Mapping—0
GLIME: A new graphical methodology for interpretable model-agnostic explanations—0
Global Lightning-Ignited Wildfires Prediction and Climate Change Projections based on Explainable Machine Learning Models—0
GNN-XAR: A Graph Neural Network for Explainable Activity Recognition in Smart Homes—0
Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning—0
GPU-Accelerated Rule Evaluation and Evolution—0
GraphIX: Graph-based In silico XAI(explainable artificial intelligence) for drug repositioning from biopharmaceutical network—0
Guarding Digital Privacy: Exploring User Profiling and Security Enhancements—0
Guarding the Gate: ConceptGuard Battles Concept-Level Backdoors in Concept Bottleneck Models—0
Hardware Acceleration of Explainable Artificial Intelligence—0
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