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Detecting and explaining unfairness in consumer contracts through memory networks

Detecting and explaining unfairness in consumer contracts through memory networks 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence and Law Springer Journals

Detecting and explaining unfairness in consumer contracts through memory networks

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Publisher
Springer Journals
Copyright
Copyright © The Author(s) 2021
ISSN
0924-8463
eISSN
1572-8382
DOI
10.1007/s10506-021-09288-2
Publisher site
See Article on Publisher Site

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.

Journal

Artificial Intelligence and LawSpringer Journals

Published: Mar 1, 2022

Keywords: Unfair clause detection; Deep learning; Memory networks; Explainability; Legal rationales

References