A Concept and Argumentation based Interpretable Model in High Risk Domains

• URL: http://arxiv.org/abs/2208.08149v1
• Date: Wed, 17 Aug 2022 08:29:02 GMT
• Title: A Concept and Argumentation based Interpretable Model in High Risk Domains
• Authors: Haixiao Chi, Dawei Wang, Gaojie Cui, Feng Mao, Beishui Liao
• Abstract summary: Interpretability has become an essential topic for artificial intelligence in high-risk domains such as healthcare, bank and security. We propose a concept and argumentation based model (CAM) that includes a novel concept mining method to obtain human understandable concepts. CAM provides decisions that are based on human-level knowledge and the reasoning process is intrinsically interpretable.
• Score: 9.209499864585688
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Interpretability has become an essential topic for artificial intelligence in some high-risk domains such as healthcare, bank and security. For commonly-used tabular data, traditional methods trained end-to-end machine learning models with numerical and categorical data only, and did not leverage human understandable knowledge such as data descriptions. Yet mining human-level knowledge from tabular data and using it for prediction remain a challenge. Therefore, we propose a concept and argumentation based model (CAM) that includes the following two components: a novel concept mining method to obtain human understandable concepts and their relations from both descriptions of features and the underlying data, and a quantitative argumentation-based method to do knowledge representation and reasoning. As a result of it, CAM provides decisions that are based on human-level knowledge and the reasoning process is intrinsically interpretable. Finally, to visualize the purposed interpretable model, we provide a dialogical explanation that contain dominated reasoning path within CAM. Experimental results on both open source benchmark dataset and real-word business dataset show that (1) CAM is transparent and interpretable, and the knowledge inside the CAM is coherent with human understanding; (2) Our interpretable approach can reach competitive results comparing with other state-of-art models.

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