Explainable AI(XAI)is a domain focused on providing interpretability and explainability of a decision-making process. In the domain of law, in addition to system and data transparency, it also requires the (legal-) decision-model transparency and the ability to understand the models inner working when arriving at the decision. This paper provides the first approaches to using a popular image 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.
@article{arxiv.2012.09603,
title = {Towards Grad-CAM Based Explainability in a Legal Text Processing Pipeline},
author = {Lukasz Gorski and Shashishekar Ramakrishna and Jedrzej M. Nowosielski},
journal= {arXiv preprint arXiv:2012.09603},
year = {2020}
}
Comments
Workshop on EXplainable & Responsible AI in Law (XAILA) at 33rd International Conference on Legal Knowledge and Information Systems (JURIX)