Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/139223
Title: Building more explainable artificial intelligence with argumentation
Authors: Zeng, Zhiwei
Miao, Chunyan
Leung, Cyril
Chin, Jing Jih
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Zeng, Z., Miao, C., Leung, C., & Chin, J. J. (2018). Building more explainable artificial intelligence with argumentation. Proceedings of The Twenty-Third AAAI/SIGAI Doctoral Consortium.
Abstract: Currently, much of machine learning is opaque, just like a “black box”. However, in order for humans to understand, trust and effectively manage the emerging AI systems, an AI needs to be able to explain its decisions and conclusions. In this paper, I propose an argumentation-based approach to explainable AI, which has the potential to generate more comprehensive explanations than existing approaches.
URI: https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16762
https://hdl.handle.net/10356/139223
Rights: © 2018 Association for the Advancement of Artificial Intelligence. All rights reserved. This paper was published in The Twenty-Third AAAI/SIGAI Doctoral Consortium and is made available with permission of Association for the Advancement of Artificial Intelligence.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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