论文标题
用于消费者保护的存储网络：不公平暴露
Memory networks for consumer protection:unfairness exposed
论文作者
论文摘要
最近的工作表明，数据驱动的AI方法如何通过支持对法律文件的自动分析来利用消费者保护。但是，数据驱动方法的缺点是可解释性差。我们认为，在这个领域中，可以通过诉诸法律原理来提供分类器结果的有用解释。因此，我们考虑了记忆增强神经网络的几种配置，其中理由在上下文知识的建模中具有特殊的作用。我们的结果表明，理由不仅有助于提高分类准确性，而且还能够提供有意义的自然语言解释其他不透明的分类器结果。
Recent work has demonstrated how data-driven AI methods can leverage consumer protection by supporting the automated analysis of legal documents. However, a shortcoming of data-driven approaches is poor explainability. We posit that in this domain useful explanations of classifier outcomes can be provided by resorting to legal rationales. We thus consider several configurations of memory-augmented neural networks where rationales are given a special role in the modeling of context knowledge. Our results show that rationales not only contribute to improve the classification accuracy, but are also able to offer meaningful, natural language explanations of otherwise opaque classifier outcomes.
