Memory networks for consumer protection:unfairness exposed

TL;DR

This paper explores the use of memory-augmented neural networks with legal rationales to improve classification accuracy and provide natural language explanations in consumer protection legal document analysis.

Contribution

It introduces configurations of memory networks that incorporate legal rationales, enhancing both explainability and accuracy in classifying consumer protection documents.

Findings

Rationales improve classification accuracy.

Memory networks provide meaningful explanations.

Enhanced interpretability of legal document analysis.

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.