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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 851–875 of 971 papers

TitleStatusHype
DLinear-based Prediction of Remaining Useful Life of Lithium-Ion Batteries: Feature Engineering through Explainable Artificial Intelligence—0
Doctor-in-the-Loop: An Explainable, Multi-View Deep Learning Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer—0
Does Explainable Artificial Intelligence Improve Human Decision-Making?—0
Do humans and Convolutional Neural Networks attend to similar areas during scene classification: Effects of task and image type—0
Domain Knowledge Aided Explainable Artificial Intelligence for Intrusion Detection and Response—0
Drug discovery with explainable artificial intelligence—0
Easydiagnos: a framework for accurate feature selection for automatic diagnosis in smart healthcare—0
ECLAD: Extracting Concepts with Local Aggregated Descriptors—0
Eclectic Rule Extraction for Explainability of Deep Neural Network based Intrusion Detection Systems—0
Efficient XAI Techniques: A Taxonomic Survey—0
Elucidating Discrepancy in Explanations of Predictive Models Developed using EMR—0
Emergent Explainability: Adding a causal chain to neural network inference—0
Employing Explainable Artificial Intelligence (XAI) Methodologies to Analyze the Correlation between Input Variables and Tensile Strength in Additively Manufactured Samples—0
Empowering Prior to Court Legal Analysis: A Transparent and Accessible Dataset for Defensive Statement Classification and Interpretation—0
Enabling Machine Learning Algorithms for Credit Scoring -- Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models—0
Enhanced Prototypical Part Network (EPPNet) For Explainable Image Classification Via Prototypes—0
Enhancing AI Transparency: XRL-Based Resource Management and RAN Slicing for 6G ORAN Architecture—0
Enhancing Breast Cancer Diagnosis in Mammography: Evaluation and Integration of Convolutional Neural Networks and Explainable AI—0
Enhancing Cancer Diagnosis with Explainable & Trustworthy Deep Learning Models—0
Enhancing Counterfactual Image Generation Using Mahalanobis Distance with Distribution Preferences in Feature Space—0
Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution—0
Enhancing UAV Security Through Zero Trust Architecture: An Advanced Deep Learning and Explainable AI Analysis—0
Ensembles of Convolutional Neural Networks models for pediatric pneumonia diagnosis—0
Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing—0
Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios—0
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