Related papers: Memory networks for consumer protection:unfairness exposed

Memory networks for consumer protection:unfairness exposed

URL: http://arxiv.org/abs/2008.07346v1
Date: Fri, 24 Jul 2020 14:25:54 GMT
Title: Memory networks for consumer protection:unfairness exposed
Authors: Federico Ruggeri, Francesca Lagioia, Marco Lippi, Paolo Torroni
Abstract summary: Data-driven AI methods can leverage consumer protection by supporting the automated analysis of legal documents. We 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.
Score: 12.884439911728112
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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