Explainable NLLP: Advancements in Explainable AI for Natural Legal Language Processing

Lucas Resck, Felipe Moreno-Vera, Tobias Veiga, Gerardo Paucar, Ezequiel Fajreldines, Guilherme Klafke, Luis Gustavo Nonato, Jorge Poco

Abstract: Despite the increasing application of machine learning and NLP methods in the legal domain, there has been limited effort to enhance the understanding and transparency of these algorithms. This paper addresses this gap by presenting a survey on Explainable AI (XAI) applied to Natural Legal Language Processing (NLLP). To our knowledge, this survey represents the first comprehensive examination at the intersection of XAI, Law, and NLP. Building upon prior surveys focused on partial intersections of these domains, we propose a taxonomy for classifying papers based on the NLLP task, explanation type, and technique employed. Additionally, we delve into discussions surrounding Explainable NLLP, considering perspectives related to ethics, current open issues, and future work. Our analysis reveals that the categorized papers generally do not thoroughly examine the ethical implications of the explainability principle in NLP within the legal field. Furthermore, they do not discuss the role and value of explanations nor do they effectively utilize their respective XAI techniques to offer insights into the limitations of NLP systems.