XAI—Explainable artificial intelligence-Reference-Cited by-同舟云学术

XAI—Explainable artificial intelligence

Published:2019-12-18 Issue:37 Volume:4 Page:
ISSN:2470-9476
Container-title:Science Robotics
language:en
Short-container-title:Sci. Robot.

Author:

Gunning David¹^ORCID,Stefik Mark²^ORCID,Choi Jaesik³^ORCID,Miller Timothy⁴^ORCID,Stumpf Simone⁵,Yang Guang-Zhong⁶^ORCID

Affiliation:

1. Defense Advanced Research Projects Agency (DARPA), 675 North Randolph Street, Arlington, VA 22201, USA.

2. Palo Alto Research Center, 3333 Coyote Hill Road, Palo Alto, CA 94304, USA.

3. Graduate School of Artificial Intelligence, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.

4. School of Computing and Information Systems, University of Melbourne, Victoria 3010, Australia.

5. Centre for HCI Design, School of Mathematics, Computer Science and Engineering, City, University of London, London EC1V 0HB, UK.

6. Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China.

Abstract

Explainability is essential for users to effectively understand, trust, and manage powerful artificial intelligence applications.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Mechanical Engineering

Reference9 articles.

1. W. Samek G. Montavon A. Vedaldi L. K. Hansen K. R. Muller Explainable AI: Interpreting Explaining and Visualizing Deep Learning (Springer Nature 2019).

2. H. J. Escalante S. Escalera I. Guyon X. Baró Y. Güçlütürk U. Güçlü M. van Gerven Explainable and Interpretable Models in Computer Vision and Machine Learning (Springer 2018).

3. O. Biran C. Cotton Explanation and justification in machine learning: A survey paper presented at the IJCAI-17 Workshop on Explainable AI (XAI) Melbourne Australia 20 August 2017.

4. Intelligibility and Accountability: Human Considerations in Context-Aware Systems

5. T. Kulesza M. Burnett W. Wong S. Stumpf Principles of explanatory debugging to personalize interactive machine learning in Proceedings of the 20th International Conference on Intelligent User Interfaces (ACM 2015) pp. 126–137.

Cited by 1070 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Survey on Explainable AI: Techniques, challenges and open issues;Expert Systems with Applications;2024-12

2. Using artificial intelligence and predictive modelling to enable learning healthcare systems (LHS) for pandemic preparedness;Computational and Structural Biotechnology Journal;2024-12

3. Explainable AI for government: Does the type of explanation matter to the accuracy, fairness, and trustworthiness of an algorithmic decision as perceived by those who are affected?;Government Information Quarterly;2024-12

4. Decoding micro-electrocorticographic signals by using explainable 3D convolutional neural network to predict finger movements;Journal of Neuroscience Methods;2024-11

5. Attribution of hydrological droughts in large river-connected lakes: Insights from an explainable machine learning model;Science of The Total Environment;2024-11