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Explainable Artificial Intelligence (XAI)

Explainable Artificial Intelligence

Papers

Showing 176–200 of 1041 papers

TitleStatusHype
Argumentation Theoretical Frameworks for Explainable Artificial Intelligence—0
Argumentative XAI: A Survey—0
Detecting AI-Generated Text in Educational Content: Leveraging Machine Learning and Explainable AI for Academic Integrity—0
Cognitive Perspectives on Context-based Decisions and Explanations—0
Pruning coupled with learning, ensembles of minimal neural networks, and future of XAI—0
Are Linear Regression Models White Box and Interpretable?—0
Characterizing Human Explanation Strategies to Inform the Design of Explainable AI for Building Damage Assessment—0
Combining Counterfactuals With Shapley Values To Explain Image Models—0
A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME—0
A Survey of Accessible Explainable Artificial Intelligence Research—0
Comparative Analysis of Hyperspectral Image Reconstruction Using Deep Learning for Agricultural and Biological Applications—0
Concept-Attention Whitening for Interpretable Skin Lesion Diagnosis—0
Concept Embedding Analysis: A Review—0
Concept Induction using LLMs: a user experiment for assessment—0
A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecasting—0
Bridging the Transparency Gap: What Can Explainable AI Learn From the AI Act?—0
Deep Learning Reproducibility and Explainable AI (XAI)—0
Context-dependent Explainability and Contestability for Trustworthy Medical Artificial Intelligence: Misclassification Identification of Morbidity Recognition Models in Preterm Infants—0
A survey on Concept-based Approaches For Model Improvement—0
Contextual Trust—0
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI—0
Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma—0
Convolutional Neural Network Interpretability with General Pattern Theory—0
Are Large Language Models the New Interface for Data Pipelines?—0
Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models—0
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