Related papers: Self-explaining deep models with logic rule reasoning

Self-explaining deep models with logic rule reasoning

URL: http://arxiv.org/abs/2210.07024v2
Date: Mon, 17 Oct 2022 08:24:42 GMT
Title: Self-explaining deep models with logic rule reasoning
Authors: Seungeon Lee, Xiting Wang, Sungwon Han, Xiaoyuan Yi, Xing Xie, Meeyoung Cha
Abstract summary: We present SELOR, a framework for integrating self-explaining capabilities into a given deep model. By "human precision", we refer to the degree to which humans agree with the reasons models provide for their predictions.
Score: 34.26828172603353
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We present SELOR, a framework for integrating self-explaining capabilities into a given deep model to achieve both high prediction performance and human precision. By "human precision", we refer to the degree to which humans agree with the reasons models provide for their predictions. Human precision affects user trust and allows users to collaborate closely with the model. We demonstrate that logic rule explanations naturally satisfy human precision with the expressive power required for good predictive performance. We then illustrate how to enable a deep model to predict and explain with logic rules. Our method does not require predefined logic rule sets or human annotations and can be learned efficiently and easily with widely-used deep learning modules in a differentiable way. Extensive experiments show that our method gives explanations closer to human decision logic than other methods while maintaining the performance of deep learning models.

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