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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 651–700 of 971 papers

TitleStatusHype
Position: Explain to Question not to Justify—0
Explain yourself! Effects of Explanations in Human-Robot Interaction—0
Explanation-based Counterfactual Retraining(XCR): A Calibration Method for Black-box Models—0
Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing—0
Explanation in Artificial Intelligence: Insights from the Social Sciences—0
"Explanation" is Not a Technical Term: The Problem of Ambiguity in XAI—0
Explanation User Interfaces: A Systematic Literature Review—0
Exploiting Explanations for Model Inversion Attacks—0
Exploring a Gradient-based Explainable AI Technique for Time-Series Data: A Case Study of Assessing Stroke Rehabilitation Exercises—0
Exploring Energy Landscapes for Minimal Counterfactual Explanations: Applications in Cybersecurity and Beyond—0
Exploring Explainable AI in the Financial Sector: Perspectives of Banks and Supervisory Authorities—0
Extending XReason: Formal Explanations for Adversarial Detection—0
ExTRA: Explainable Therapy-Related Annotations—0
FairLens: Auditing Black-box Clinical Decision Support Systems—0
False Sense of Security in Explainable Artificial Intelligence (XAI)—0
Fault Diagnosis using eXplainable AI: a Transfer Learning-based Approach for Rotating Machinery exploiting Augmented Synthetic Data—0
Feature Visualization within an Automated Design Assessment leveraging Explainable Artificial Intelligence Methods—0
Financial Fraud Detection Using Explainable AI and Stacking Ensemble Methods—0
Finding Words Associated with DIF: Predicting Differential Item Functioning using LLMs and Explainable AI—0
Fiper: a Visual-based Explanation Combining Rules and Feature Importance—0
Flood Prediction and Analysis on the Relevance of Features using Explainable Artificial Intelligence—0
FNDEX: Fake News and Doxxing Detection with Explainable AI—0
Focal Cortical Dysplasia Type II Detection Using Cross Modality Transfer Learning and Grad-CAM in 3D-CNNs for MRI Analysis—0
Formal Explanations for Neuro-Symbolic AI—0
Forma mentis networks predict creativity ratings of short texts via interpretable artificial intelligence in human and GPT-simulated raters—0
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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