Towards Grad-CAM Based Explainability in a Legal Text Processing Pipeline

Explainable AI (XAI) is a domain focused on providing interpretabil- ity and explainability of a decision-making process. In the domain of law, in addi- tion to system and data transparency, it also requires the (legal-) decision-model transparency and the ability to understand the model’s inner working when arriv- ing at the decision. This paper provides the first approaches to using a popular im- age processing technique, Grad-CAM, to showcase the explainability concept for legal texts. With the help of adapted Grad-CAM metrics, we show the interplay between the choice of embeddings, its consideration of contextual information, and their effect on downstream processing.