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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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Reward is enough

Artificial intelligence, 2021-10, Vol.299, p.103535, Article 103535

2021

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Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Information fusion, 2020-06, Vol.58, p.82-115

2020

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Evaluating XAI: A comparison of rule-based and example-based explanations

Artificial intelligence, 2021-02, Vol.291, p.103404, Article 103404

2021

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There is a blind spot in AI research

Nature (London), 2016-10, Vol.538 (7625), p.311-313

2016

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Deep learning in neural networks: An overview

Neural networks, 2015-01, Vol.61, p.85-117

Schmidhuber, Jürgen

2015

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Explainability in deep reinforcement learning

Knowledge-based systems, 2021-02, Vol.214, p.106685, Article 106685

2021

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Introduction to the Special Issue on Multiagent Systems and Services in the Internet of Things

ACM transactions on Internet technology, 2023-03, Vol.22 (4), p.1-3, Article 99

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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Trends in extreme learning machines: A review

Neural networks, 2015-01, Vol.61, p.32-48

2015

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Group to establish standards for AI in papers

Science (American Association for the Advancement of Science), 2024-04, Vol.384 (6693), p.261-261

Else, Holly

2024

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Clinical, Legal, and Ethical Aspects of Artificial Intelligence-Assisted Conversational Agents in Health Care

JAMA : the journal of the American Medical Association, 2020-08, Vol.324 (6), p.552

2020

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Artificial intelligence in medicine

Metabolism, clinical and experimental, 2017-04, Vol.69, p.S36-S40

2017

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From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where

IEEE transactions on industrial informatics, 2022-08, Vol.18 (8), p.5031-5042

2022

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Explanation in artificial intelligence: Insights from the social sciences

Artificial intelligence, 2019-02, Vol.267, p.1-38

Miller, Tim

2019

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Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association

The Journal of pathology, 2019-11, Vol.249 (3), p.286-294

2019

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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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Distributed Constraint Optimization Problems and Applications: A Survey

The Journal of artificial intelligence research, , Vol.61, p.623-698

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What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research

Artificial intelligence, 2021-07, Vol.296, p.103473, Article 103473

2021

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Autonomous agents modelling other agents: A comprehensive survey and open problems

Artificial intelligence, 2018-05, Vol.258, p.66-95

2018

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Vision, challenges, roles and research issues of Artificial Intelligence in Education

Computers and education. Artificial intelligence, 2020, Vol.1, p.100001, Article 100001

2020

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From Word To Sense Embeddings: A Survey on Vector Representations of Meaning

The Journal of artificial intelligence research, 2018-12, Vol.63, p.743-788

2018

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SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary

The Journal of artificial intelligence research, 2018-04, Vol.61, p.863-905

2018

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Confident Learning: Estimating Uncertainty in Dataset Labels

The Journal of artificial intelligence research, 2021-04, Vol.70, p.1373-1411

2021

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Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

Applied intelligence (Dordrecht, Netherlands), 2021-02, Vol.51 (2), p.854-864

2021

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Levels of explainable artificial intelligence for human-aligned conversational explanations

Reward is enough

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Evaluating XAI: A comparison of rule-based and example-based explanations

There is a blind spot in AI research

Deep learning in neural networks: An overview

Explainability in deep reinforcement learning

Introduction to the Special Issue on Multiagent Systems and Services in the Internet of Things

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

Trends in extreme learning machines: A review

Group to establish standards for AI in papers

Clinical, Legal, and Ethical Aspects of Artificial Intelligence-Assisted Conversational Agents in Health Care

Artificial intelligence in medicine

From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where

Explanation in artificial intelligence: Insights from the social sciences

Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association

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

Distributed Constraint Optimization Problems and Applications: A Survey

What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research

Autonomous agents modelling other agents: A comprehensive survey and open problems

Vision, challenges, roles and research issues of Artificial Intelligence in Education

From Word To Sense Embeddings: A Survey on Vector Representations of Meaning

SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary

Confident Learning: Estimating Uncertainty in Dataset Labels

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

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