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Building More Explainable Artificial Intelligence with Argumentation


Authors: Z. Zeng, C. Miao, C. Leung, and J. J. Chin
Title: Building More Explainable Artificial Intelligence with Argumentation
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.
Keywords: 
Conference Name: 32nd AAAI Conference on Artificial Intelligence (AAAI'18)
Location: New Orleans, USA
Publisher: AAAI Press
Year: 2018
Accepted PDF File: Building_More_Explainable_Artificial_Intelligence_with_Argumentation_accepted.pdf
Permanent Link: https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16762
Reference: Z. Zeng, C. Miao, C. Leung, and J. J. Chin, “Building more explainable artificial intelligence with argumentation,” in Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI’18). AAAI Press, February 2018, pp. 8044–8045.
bibtex: 
@inproceedings{LILY-c146, 
    author	= {Zeng, Zhiwei and Miao, Chunyan and Leung, Cyril and Chin, Jing Jih},
    title	= {Building More Explainable Artificial Intelligence with Argumentation},  
    booktitle	= {Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18)}, 
    year		= {2018}, 
    month	= {February}, 
    pages	= {8044-8045}, 
    location	= {New Orleans, USA},
    publisher	= {AAAI Press},
 }