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Towards Human-Centered Explainable AI: A Survey of User Studies for Model Explanations

IEEE transactions on pattern analysis and machine intelligence, 2024-04, Vol.46 (4), p.1-20

2024

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A Survey on Medical Explainable AI (XAI): Recent Progress, Explainability Approach, Human Interaction and Scoring System

Sensors (Basel, Switzerland), 2022-10, Vol.22 (20), p.8068

2022

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Survey on ontology-based explainable AI in manufacturing

Journal of intelligent manufacturing, 2024-02

2024

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Explainable AI: A Brief Survey on History, Research Areas, Approaches and Challenges

Natural Language Processing and Chinese Computing, p.563-574

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Survey of XAI in Digital Pathology

Artificial Intelligence and Machine Learning for Digital Pathology, 2020, Vol.12090, p.56-88

2020

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Interpretable Time Series Models for Wastewater Modeling in Combined Sewer Overflows

Procedia computer science, 2024, Vol.237, p.155-162

2024

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Levels of explainable artificial intelligence for human-aligned conversational explanations

Artificial intelligence, 2021-10, Vol.299, p.103525, Article 103525

2021

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Assessing fidelity in XAI post-hoc techniques: A comparative study with ground truth explanations datasets

Artificial intelligence, 2024-10, Vol.335, p.104179, Article 104179

2024

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Interpretability of deep neural networks: A review of methods, classification and hardware

Neurocomputing (Amsterdam), 2024-10, Vol.601, p.128204, Article 128204

2024

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Towards Explainable AI for Channel Estimation in Wireless Communications

IEEE transactions on vehicular technology, 2024-05, Vol.73 (5), p.7389-7394

2024

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On the fusion of soft-decision-trees and concept-based models

Applied soft computing, 2024-07, Vol.160, p.111632, Article 111632

2024

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Multimodal explainability via latent shift applied to COVID-19 stratification

Pattern recognition, 2024-12, Vol.156, p.110825, Article 110825

2024

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Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions

Information fusion, 2024-06, Vol.106, p.102301, Article 102301

2024

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Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities

Knowledge-based systems, 2023-03, Vol.263, p.110273, Article 110273

2023

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Tamp-X: Attacking explainable natural language classifiers through tampered activations

Computers & security, 2022-09, Vol.120, p.102791, Article 102791

2022

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Explainable AI: A Review of Machine Learning Interpretability Methods

Entropy (Basel, Switzerland), 2020-12, Vol.23 (1), p.18

2020

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Trainable Noise Model as an Explainable Artificial Intelligence Evaluation Method: Application on Sobol for Remote Sensing Image Segmentation

Environmental Sciences Proceedings, 2023-11, Vol.29 (1), p.49

2023

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Evaluating the necessity of the multiple metrics for assessing explainable AI: A critical examination

Neurocomputing (Amsterdam), 2024-10, Vol.602, p.128282, Article 128282

2024

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Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

Information fusion, 2023-11, Vol.99, p.101805, Article 101805

2023

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Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications

Proceedings of the IEEE, 2021-03, Vol.109 (3), p.247-278

2021

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Pruning by explaining: A novel criterion for deep neural network pruning

Pattern recognition, 2021-07, Vol.115, p.107899, Article 107899

2021

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A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems

ACM transactions on interactive intelligent systems, 2021-12, Vol.11 (3-4), p.1-45

2021

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Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability

International journal of information management, 2023-04, Vol.69, p.102538, Article 102538

2023

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A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

IEEE transaction on neural networks and learning systems, 2021-11, Vol.32 (11), p.4793-4813

2021

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Accurate & simple open-sourced no-code machine learning and CDFT predictive models for the antioxidant activity of phenols

Computational and theoretical chemistry, 2024-09, Vol.1239, p.114782, Article 114782

2024

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Towards Human-Centered Explainable AI: A Survey of User Studies for Model Explanations

A Survey on Medical Explainable AI (XAI): Recent Progress, Explainability Approach, Human Interaction and Scoring System

Survey on ontology-based explainable AI in manufacturing

Explainable AI: A Brief Survey on History, Research Areas, Approaches and Challenges

Survey of XAI in Digital Pathology

Interpretable Time Series Models for Wastewater Modeling in Combined Sewer Overflows

Levels of explainable artificial intelligence for human-aligned conversational explanations

Assessing fidelity in XAI post-hoc techniques: A comparative study with ground truth explanations datasets

Interpretability of deep neural networks: A review of methods, classification and hardware

Towards Explainable AI for Channel Estimation in Wireless Communications

On the fusion of soft-decision-trees and concept-based models

Multimodal explainability via latent shift applied to COVID-19 stratification

Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions

Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities

Tamp-X: Attacking explainable natural language classifiers through tampered activations

Explainable AI: A Review of Machine Learning Interpretability Methods

Trainable Noise Model as an Explainable Artificial Intelligence Evaluation Method: Application on Sobol for Remote Sensing Image Segmentation

Evaluating the necessity of the multiple metrics for assessing explainable AI: A critical examination

Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications

Pruning by explaining: A novel criterion for deep neural network pruning

A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems

Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

Accurate & simple open-sourced no-code machine learning and CDFT predictive models for the antioxidant activity of phenols

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