Rūta Liepiņa

Legal principles stated in judicial decisions are a key reference for judges and legal professionals. However, typically, decisions are long and complex documents. The automatic extraction of legal principles therein may thus be beneficial to many. Unfortunately, this is an under-investigated task for which few resources and implementations exist, especially with regard to EU law. To promote research in this domain, we develop a novel corpus of 64 judicial decisions on Fiscal State Aid by the CJEU (the Court of Justice of the European Union), in which legal principles are identified and tagged. With this new resource, we conduct extensive experimentation to identify the best combinations of representation methods, classifiers and data augmentation methods for the task at hand, obtaining an F1 score of 0.72. Additionally, we consider the collections of legal principles obtained by running the extractive task and collating the output paragraphs together. We show, through the use of lexical metrics and human evaluation, that the quality of generated collections is comparable to that of expert-made ones.

Disputes over causes play a central role in legal argumentation and liability attribution. Legal approaches to causation often struggle to capture cause-in-fact in complex situations, e.g. overdetermination, preemption, omission. In this paper, we first assess three current theories of causation to illustrate their strengths and weaknesses in capturing cause-in-fact. Secondly, we introduce a semi-formal framework for modelling causal arguments through strict and defeasible rules. Thirdly, the framework is applied to the Althen vaccine injury case. And lastly, we discuss the need for new criteria based on a common causal argumentation framework and propose ideas on how to integrate the current theories of causation to assess the strength of causal arguments, while also acknowledging the tension between evidence-based and policy-based causal analysis in law.

In a world of human-only readers, a trade-off persists between comprehensiveness and comprehensibility: only privacy policies too long to be humanly readable can precisely describe the intended data processing. We argue that this trade-off no longer exists where LLMs are able to extract tailored information from clearly-drafted fully-comprehensive privacy policies. To substantiate this claim, we provide a methodology for drafting comprehensive non-ambiguous privacy policies and for querying them using LLMs prompts. Our methodology is tested with an experiment aimed at determining to what extent GPT-4 and Llama2 are able to answer questions regarding the content of privacy policies designed in the format we propose. We further support this claim by analyzing real privacy policies in the chosen market sectors through two experiments (one with legal experts, and another by using LLMs). Based on the success of our experiments, we submit that data protection law should change: it must require controllers to provide clearly drafted, fully comprehensive privacy policies from which data subjects and other actors can extract the needed information, with the help of LLMs.

The debates on ethics of AI and the need for AI regulation are unfolding across the world. Algorithms are extensively employed to make decisions that have profound impacts on societies and individuals, and these lead to social discrimination, violation of privacyPrivacy and property rights, algorithmic biases, censorship, manipulation, etc. These concerns and challenges sparked academic debates and triggered regulating AI and designing digital ethicsDigital ethics. There is a necessity to develop regulations on AI governanceAI governance and prepare for a world with many more privacyPrivacy and moral issues, copyright violations, and externalities than exist today.