为难懂的深度学习模型创建可读的孪生模型，提升AI可解释性。
Readable Twins of Unreadable Models
- 用不确定信息流模型构建深度学习模型的可读孪生体。
- 在MNIST手写数字识别任务中成功实现从DLM到IIFM的转换。
- 适合关注AI可解释性与模型透明度的研究者。
构建负责任的人工智能（AI）系统是当前人工智能研究与开发的重要议题。负责任AI的一个关键特性是可解释性。本文关注可解释深度学习（XDL）系统。基于物理对象数字孪生的思想，我们提出为难以理解的深度学习模型（DLM）创建可读孪生体（以不确定信息流模型IIFM的形式）。文中完整展示了从DLM到不确定信息流模型（IIFM）的转换流程，并以手写数字识别的MNIST数据集上的深度学习分类模型为例进行了说明。
原文摘要 · Abstract (English)
Creating responsible artificial intelligence (AI) systems is an important issue in contemporary research and development of works on AI. One of the characteristics of responsible AI systems is their explainability. In the paper, we are interested in explainable deep learning (XDL) systems. On the basis of the creation of digital twins of physical objects, we introduce the idea of creating readable twins (in the form of imprecise information flow models) for unreadable deep learning models. The complete procedure for switching from the deep learning model (DLM) to the imprecise information flow model (IIFM) is presented. The proposed approach is illustrated with an example of a deep learning classification model for image recognition of handwritten digits from the MNIST data set.
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