Lukasz Gorski

Lukasz Gorski, Shashishekar Ramakrishna, Jedrzej M. Nowosielski

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.

Shashishekar Ramakrishna, Lukasz Gorski, Adrian Paschke

Despite the 'apparent clarity' of a given legal provision, its application may result in an outcome that does not exactly conform to the semantic level of a statute. The vagueness within a legal text is induced intentionally to accommodate all possible scenarios under which such norms should be applied, thus making the role of pragmatics an important aspect also in the representation of a legal norm and reasoning on top of it. The notion of pragmatics considered in this paper does not focus on the aspects associated with judicial decision making. The paper aims to shed light on the aspects of pragmatics in legal linguistics, mainly focusing on the domain of patent law, only from a knowledge representation perspective. The philosophical discussions presented in this paper are grounded based on the legal theories from Grice and Marmor.