  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.
