The first issue of Artificial Intelligence and Law journal was published in 1992. This paper provides commentaries on nine significant papers drawn from the Journal’s second decade. Four of the papers relate to reasoning with legal cases, introducing contextual considerations, predicting outcomes on the basis of natural language descriptions of the cases, comparing different ways of representing cases, and formalising precedential reasoning. One introduces a method of analysing arguments that was to become very widely used in AI and Law, namely (...) argumentation schemes. Two relate to ontologies for the representation of legal concepts and two take advantage of the increasing availability of legal corpora in this decade, to automate document summarisation and for the mining of arguments. (shrink)

Evidences are important proofs to support judicial trials. Automatically extracting evidences from judgement documents can be used to assess the trial quality and support “Intelligent Court”. Current evidence extraction is primarily depended on sequence labelling models. Despite their success, they can only assign a label to a token, which is difficult to recognize nested evidence entities in judgment documents, where a token may belong to several evidences at the same time. In this paper, we present a novel evidence extraction architecture (...) called ATT-MRC, in which extracting evidence entities is formalized as a question answer problem, where all evidence spans are screened out as possible correct answers. Furthermore, to address the data imbalance problem in the judgement documents, we revised the loss function and combined it with a data enhancement technique. Experimental results demonstrate that our model has better performance than related works in evidence extraction. (shrink)

Transformer-based language models (TLMs) have widely been recognized to be a cutting-edge technology for the successful development of deep-learning-based solutions to problems and applications that require natural language processing and understanding. Like for other textual domains, TLMs have indeed pushed the state-of-the-art of AI approaches for many tasks of interest in the legal domain. Despite the first Transformer model being proposed about six years ago, there has been a rapid progress of this technology at an unprecedented rate, whereby BERT and (...) related models represent a major reference, also in the legal domain. This article provides the first systematic overview of TLM-based methods for AI-driven problems and tasks in the legal sphere. A major goal is to highlight research advances in this field so as to understand, on the one hand, how the Transformers have contributed to the success of AI in supporting legal processes, and on the other hand, what are the current limitations and opportunities for further research development. (shrink)

The first issue of _Artificial Intelligence and Law_ journal was published in 1992. This special issue marks the 30th anniversary of the journal by reviewing the progress of the field through thirty commentaries on landmark papers and groups of papers from that journal.

Court judgments contain valuable information on how statutory laws and past court precedents are interpreted and how the interdependence structure among them evolves in the courtroom. Data-mining the evolving structure of such customs and norms that reflect myriad social values from a large-scale court judgment corpus is an essential task from both the academic and industrial perspectives. In this paper, using data from approximately 110,000 court judgments from Japan spanning the period 1998–2018 from the district to the supreme court level, (...) we propose two tasks that grasp such a structure from court judgments and highlight the strengths and weaknesses of major machine learning models. One is a prediction task based on masked language modeling that connects textual information to legal codes and past court precedents. Another is a dynamic link prediction task where we predict the hidden interdependence structure in the law. We make quantitative and qualitative comparisons among major machine learning models to obtain insights for future developments. (shrink)

Accurate data annotation is essential to successfully implementing machine learning (ML) for regulatory compliance. Annotations allow organizations to train supervised ML algorithms and to adapt and audit the software they buy. The lack of annotation tools focused on regulatory data is slowing the adoption of established ML methodologies and process models, such as CRISP-DM, in various legal domains, including in regulatory compliance. This article introduces Ant, an open-source annotation software for regulatory compliance. Ant is designed to adapt to complex organizational (...) processes and enable compliance experts to be in control of ML projects. By drawing on Business Process Modeling (BPM), we show that Ant can contribute to lift major technical bottlenecks to effectively implement regulatory compliance through software, such as the access to multiple sources of heterogeneous data and the integration of process complexities in the ML pipeline. We provide empirical data to validate the performance of Ant, illustrate its potential to speed up the adoption of ML in regulatory compliance, and highlight its limitations. (shrink)

Financial risks are among the most important risks in the construction industry projects, which significantly impact project objectives, including project cost. Besides, financial risks have many interactions with each other and project parameters, which must be taken into account to analyze risks correctly. In addition, a source of financial risks in a project is the contract, which is the most important project document. Identifying terms related to financial risks in a contract and considering their effects on the risk management process (...) is an essential issue that has been neglected. Hence, an integrated model for evaluating financial risks and their related contractual clauses were presented. To this end, the effect of financial risks on the project cost was simulated using a system dynamics model. Moreover, terms related to financial risks in a contract text were identified and extracted using text mining, and their effect was included in the system dynamics model. The model was implemented in a hospital construction project in Tehran as a case study, and its results were analyzed. The innovation of the research is integrating text mining and the system dynamics model to investigate the effect of financial risks and related contractual clauses on the project cost. (shrink)

Large-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 introducesLAWSUIT, a collection of 14K Italian legal verdicts with expert-authored abstractive maxims drawn from the Constitutional Court of the Italian Republic.LAWSUITpresents 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 releaseLAWSUITto foster the development and automation of real-world legal applications. (shrink)

Condensing the key message conveyed by a long document into an informative summary is particularly helpful to lawyers and legal experts. State-of-the-art approaches to legal document summarization rely on Language Models (LMs) and are mostly trained on English documents. More limited research efforts have been devoted to summarizing legal documents in languages other than English. In this work, we investigate the applicability of Large Language Models (LLMs) to summarize Italian legal news documents. We benchmark state-of-the-art abstractive summarization techniques based on (...) Language Models, Large and not, for headline and abstract generation from legal news documents. We run an extensive set of experiments on a proprietary legal dataset, evaluating the resulting summaries according to both quantitative metrics and human evaluation. As expected, latest LLMs outperform classical models such as BART, T5, particularly in terms of grammaticality and informativeness of the summary content. Fine-tuned LLMs also show a significant increase in performance, variable across law areas, compared to their zero-shot setting. Importantly, the level of specialization of the fine-tuned version already reaches a steady state after feeding the model with few hundreds of training data. (shrink)