AbstractLarge-scale public datasets are vital for driving the progress of abstractive summarization, especially in law, where documents have highly specialized jargon. However, the available resources are English-centered, limiting research advancements in other languages. This paper introduces LAWSUIT, a collection of 14K Italian legal verdicts with expert-authored abstractive maxims drawn from the Constitutional Court of the Italian Republic. LAWSUIT presents an arduous task with lengthy source texts and evenly distributed salient content. We offer extensive experiments with sequence-to-sequence and segmentation-based approaches, revealing that the latter achieve better results in full and few-shot settings. We openly release LAWSUIT to foster the development and automation of real-world legal applications.

AbstractIn the evolving landscape of legal information systems, ChatGPT-4 and other advanced conversational agents (CAs) offer the potential to disruptively transform the law industry. This study evaluates commercially available CAs within the German legal context, thereby assessing the generalizability of previous U.S.-based findings. Employing a unique corpus of 200 distinct legal tasks, ChatGPT-4 was benchmarked against Google Bard, Google Gemini, and its predecessor, ChatGPT-3.5. Human-expert and automated assessments of 4000 CA-generated responses reveal ChatGPT-4 to be the first CA to surpass the threshold of solving realistic legal tasks and passing a German business law exam. While ChatGPT-4 outperforms ChatGPT-3.5, Google Bard, and Google Gemini in both consistency and quality, the results demonstrate a considerable degree of variability, especially in complex cases with no predefined response options. Based on these findings, legal professionals should manually verify all texts produced by CAs before use. Novices must exercise caution with CA-generated legal advice, given the expertise needed for its assessment.

AbstractMany countries employ systemic methods of protecting consumers from unfair business practices. One such practice is the use of abusive clauses in business-to-consumer (B2C) contracts, which unfairly impose additional obligations on the consumer or deprive them of their due rights. This article presents an information system that utilizes artificial intelligence methods to automate contract analysis and to detect abusive clauses. The goal of the system is to support the entire administrative process, from contract acquisition, through text extraction and the recommendation of potentially abusive clauses, to the generation of official administrative documents that can be sent to court or to the owners of firms. This article focuses on on the components that use machine learning methods. The first is an intelligent crawler that is responsible for automatically detecting contract templates on websites and retrieving them into the system. The second is a document analysis module that implements a clause recommendation algorithm. The algorithm employs transformer-based language models and information retrieval methods to identify abusive passages in text. Our solution achieved first place in a competition on the automatic analysis of B2C contracts organized by the Polish Office of Competition and Consumer Protection (UOKiK), and has since been implemented as an official tool to support the contract analysis process in Poland.

AbstractThis paper explores the extension of formal accounts of precedential constraint to make use of a factor hierarchy with intermediate factors. A problem arises, however, because constraints expressed in terms of intermediate factors may give different outcomes from those expressed only using base level factors. We argue that constraints that use only base level factors yield the correct outcomes, but that intermediate factors play an important role in the justification and explanation of those outcomes. The discussion is illustrated with a running example.

AbstractWe live in exciting times for AI and Law: technical developments are moving at a breakneck pace, and at the same time, the call for more robust AI governance and regulation grows stronger. How should we as an AI & Law community navigate these dramatic developments and claims? In this Presidential Address, I present my ideas for a way forward: researching, developing and evaluating real AI systems for the legal field with researchers from AI, Law and beyond. I will demonstrate how we at the Netherlands National Police Lab AI are developing responsible AI by combining insights from different disciplines, and how this connects to the future of our field.

AbstractThis paper presents the first dataset for Japanese Legal Judgment Prediction (LJP), the Japanese Tort-case Dataset (JTD), which features two tasks: tort prediction and its rationale extraction. The rationale extraction task identifies the court’s accepting arguments from alleged arguments by plaintiffs and defendants, which is a novel task in the field. JTD is constructed based on annotated 3477 Japanese Civil Code judgments by 41 legal experts, resulting in 7978 instances with 59,697 of their alleged arguments from the involved parties. Our baseline experiments show the feasibility of the proposed two tasks, and our error analysis by legal experts identifies sources of errors and suggests future directions of the LJP research.

AbstractIn this research, patent prosecution is conceptualized as a system of reinforcement learning from human feedback. The objective of the system is to increase the likelihood for a language model to generate patent claims that have a higher chance of being granted. To showcase the controllability of the language model, the system learns from granted patents and pre-grant applications with different rewards. The status of “granted” and “pre-grant” are perceived as labeled human feedback implicitly. In addition, specific to patent drafting, the experiments in this research demonstrate the model’s capability to learn from adjusting claim length and inclusion of limiting terms for narrowing claim scope. As proof of concept, the experiments focus on claim ones only and the training data originates from a patent dataset tailored specifically for artificial intelligence. Although the available human feedback in patent prosecution are limited and the quality of generated patent text requires improvement, the experiments following the 3-stage reinforcement learning from human feedback have demonstrated that generative language models are capable of reflecting the human feedback or intent in patent prosecution. To enhance the usability of language models, the implementation in this research utilizes modern techniques that enable execution on a single consumer-grade GPU. The demonstrated proof of concept, which reduces hardware requirements, will prove valuable in the future as more human feedback in patent prosecution become available for broader use, either within patent offices or in the public domain.