Building More Explainable Artificial Intelligence With Argumentation

Authors

  • Zhiwei Zeng Nanyang Technological University
  • Chunyan Miao Nanyang Technological University
  • Cyril Leung The University of British Columbia
  • Jing Jih Chin Institute of Geriatrics and Active Ageing, Tan Tock Seng Hospital

Keywords:

Explainable AI, Argumentation, Explanation

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.

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Published

2018-04-29

How to Cite

Zeng, Z., Miao, C., Leung, C., & Chin, J. J. (2018). Building More Explainable Artificial Intelligence With Argumentation. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11353