Artificial intelligence is being utilized in many domains as of late, and the legal system is no exception. However, as it stands now, the number of well-annotated datasets pertaining to legal documents from the Supreme Court of the United States (SCOTUS) is very limited for public use. Even though the Supreme Court rulings are public domain knowledge, trying to do meaningful work with them becomes a much greater task due to the need to manually gather and process that data from scratch each time. Hence, our goal is to create a high-quality dataset of SCOTUS court cases so that they may be readily used in natural language processing (NLP) research and other data-driven applications. Additionally, recent advances in NLP provide us with the tools to build predictive models that can be used to reveal patterns that influence court decisions. By using advanced NLP algorithms to analyze previous court cases, the trained models are able to predict and classify a court's judgment given the case's facts from the plaintiff and the defendant in textual format; in other words, the model is emulating a human jury by generating a final verdict.
In this paper, we describe the Czech Court Decision Corpus (CzCDC). CzCDC is a dataset of 237,723 decisions published by the Czech apex (or top-tier) courts, namely the Supreme Court, the Supreme Administrative Court and the Constitutional Court. All the decisions were published between 1st January 1993 and 30th September 2018. Court decisions are available on the webpages of the respective courts or via commercial databases of legal information. This often leads researchers interested in these decisions to reach either to respective court or to commercial provider. This leads to delays and ad…
Indian Institute of Technology Kanpur（坎普尔印度理工学院）；Indian Institute of Science Education and Research Kolkata（加尔各答印度科学教育与研究学院）；West Bengal National University of Juridical Sciences（西孟加拉国立法科大学）
An automated system that could assist a judge in predicting the outcome of a case would help expedite the judicial process. For such a system to be practically useful, predictions by the system should be explainable. To promote research in developing such a system, we introduce ILDC (Indian Legal Documents Corpus). ILDC is a large corpus of 35k Indian Supreme Court cases annotated with original court decisions. A portion of the corpus (a separate test set) is annotated with gold standard explanations by legal experts. Based on ILDC, we propose the task of Court Judgment Prediction and Explanat…
We present a novel Transformer-based multi-agent system for simulating the judicial rulings of the 2010-2016 Supreme Court of the United States. We train nine separate models with the respective authored opinions of each supreme justice active ca. 2015 and test the resulting system on 96 real-world cases. We find our system predicts the decisions of the real-world Supreme Court with better-than-random accuracy. We further find a correlation between model accuracy with respect to individual justices and their alignment between legal conservatism & liberalism. Our methods and results hold signif…
A Benchmark & Dataset for Detecting AI-Manipulated Visual Evidence in the Court System
University of Toronto（多伦多大学）；University of Waterloo（滑铁卢大学）；University of British Columbia（不列颠哥伦比亚大学）；University of Western Ontario（西安大略大学）；University of Ottawa（渥太华大学）
We explore the idea that authoring a piece of text is an act of maximizing one's expected utility. To make this idea concrete, we consider the societally important decisions of the Supreme Court of the United States. Extensive past work in quantitative political science provides a framework for empirically modeling the decisions of justices and how they relate to text. We incorporate into such a model texts authored by amici curiae ("friends of the court" separate from the litigants) who seek to weigh in on the decision, then explicitly model their goals in a random utility model. We demonstra…
This paper conducts a comparative study on the performance of various machine learning (``ML'') approaches for classifying judgments into legal areas. Using a novel dataset of 6,227 Singapore Supreme Court judgments, we investigate how state-of-the-art NLP methods compare against traditional statistical models when applied to a legal corpus that comprised few but lengthy documents. All approaches tested, including topic model, word embedding, and language model-based classifiers, performed well with as little as a few hundred judgments. However, more work needs to be done to optimize state-of-…
University of Bucharest（布加勒斯特大学）；University of Wolverhampton（伍尔弗汉普顿大学）；Saarland University（萨尔兰大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））
In this paper, we investigate the application of text classification methods to predict the law area and the decision of cases judged by the French Supreme Court. We also investigate the influence of the time period in which a ruling was made over the textual form of the case description and the extent to which it is necessary to mask the judge's motivation for a ruling to emulate a real-world test scenario. We report results of 96% f1 score in predicting a case ruling, 90% f1 score in predicting the law area of a case, and 75.9% f1 score in estimating the time span when a ruling has been issu…
Mining Legal Arguments to Study Judicial Formalism
Center for Critical Computational Studies（批判性计算研究所以）；Goethe University Frankfurt（法兰克福歌德大学）；Department for Legal Theory and Legal Doctrines, Faculty of Law（法学院法律理论与法律学说系）；Charles University（查尔斯大学）；Technical University of Darmstadt（达姆施塔特技术大学）；Research Center Trustworthy Data Science and Security of the University Alliance Ruhr & Ruhr University Bochum（鲁尔大学博德姆大学可信数据科学与安全研究所以）
Educational Testing Service（教育考试服务中心）；University of Washington（华盛顿大学）
This study presents a corpus of turn changes between speakers in U.S. Supreme Court oral arguments. Each turn change is labeled on a spectrum of "cooperative" to "competitive" by a human annotator with legal experience in the United States. We analyze the relationship between speech features, the nature of exchanges, and the gender and legal role of the speakers. Finally, we demonstrate that the models can be used to predict the label of an exchange with moderate success. The automatic classification of the nature of exchanges indicates that future studies of turn-taking in oral arguments can …
University of Bern（伯尔尼大学）；University of Copenhagen（哥本哈根大学）
In many jurisdictions, the excessive workload of courts leads to high delays. Suitable predictive AI models can assist legal professionals in their work, and thus enhance and speed up the process. So far, Legal Judgment Prediction (LJP) datasets have been released in English, French, and Chinese. We publicly release a multilingual (German, French, and Italian), diachronic (2000-2020) corpus of 85K cases from the Federal Supreme Court of Switzerland (FSCS). We evaluate state-of-the-art BERT-based methods including two variants of BERT that overcome the BERT input (text) length limitation (up to…
Institute of Law and Technology（法律与技术研究所）；Masaryk University（马萨里克大学）；University of Pittsburgh（匹兹堡大学）
In this paper, we introduce the citation data of the Czech apex courts (Supreme Court, Supreme Administrative Court and Constitutional Court). This dataset was automatically extracted from the corpus of texts of Czech court decisions - CzCDC 1.0. We obtained the citation data by building the natural language processing pipeline for extraction of the court decision identifiers. The pipeline included the (i) document segmentation model and the (ii) reference recognition model. Furthermore, the dataset was manually processed to achieve high-quality citation data as a base for subsequent qualitati…
University of Surrey（萨里大学）；Kingfisher Labs Ltd（翠鸟实验室有限公司）；Just Access
Transcription of legal proceedings is very important to enable access to justice. However, speech transcription is an expensive and slow process. In this paper we describe part of a combined research and industrial project for building an automated transcription tool designed specifically for the Justice sector in the UK. We explain the challenges involved in transcribing court room hearings and the Natural Language Processing (NLP) techniques we employ to tackle these challenges. We will show that fine-tuning a generic off-the-shelf pre-trained Automatic Speech Recognition (ASR) system with a…
Dhirubhai Ambani Institute of Information and Communication Technology（迪鲁巴伊·安巴尼信息与通信技术学院）；Malaviya National Institute of Technology（马拉维亚国家理工学院）；Paramonic AI
Unlike the courts in western countries, public records of Indian judiciary are completely unstructured and noisy. No large scale publicly available annotated datasets of Indian legal documents exist till date. This limits the scope for legal analytics research. In this work, we propose a new dataset consisting of over 10,000 judgements delivered by the supreme court of India and their corresponding hand written summaries. The proposed dataset is pre-processed by normalising common legal abbreviations, handling spelling variations in named entities, handling bad punctuations and accurate senten…
Better Aligned with Survey Respondents or Training Data? Unveiling Political Leanings of LLMs on U.S. Supreme Court Cases
Technical University of Munich（慕尼黑技术大学）；Allen Institute for AI（艾伦人工智能研究所）；University of Washington（华盛顿大学）；LMU Munich & Munich Center for Machine Learning (MCML)（慕尼黑大学及慕尼黑机器学习中心）
We analyze publicly available US Supreme Court documents using automated stance detection. In the first phase of our work, we investigate the extent to which the Court's public-facing language is political. We propose and calculate two distinct ideology metrics of SCOTUS justices using oral argument transcripts. We then compare these language-based metrics to existing social scientific measures of the ideology of the Supreme Court and the public. Through this cross-disciplinary analysis, we find that justices who are more responsive to public opinion tend to express their ideology during oral …
The recent advances of deep learning have dramatically changed how machine learning, especially in the domain of natural language processing, can be applied to legal domain. However, this shift to the data-driven approaches calls for larger and more diverse datasets, which are nevertheless still small in number, especially in non-English languages. Here we present the first large-scale benchmark of Korean legal AI datasets, LBOX OPEN, that consists of one legal corpus, two classification tasks, two legal judgement prediction (LJP) tasks, and one summarization task. The legal corpus consists of…
Tsinghua University（清华大学）；Beijing Powerlaw Intelligent Technology Co., Ltd.（北京幂律智能科技有限责任公司）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；China Justice Big Data Institute（中国司法大数据研究院）；Supreme People Court（最高人民法院）
In this paper, we introduce CAIL2019-SCM, Chinese AI and Law 2019 Similar Case Matching dataset. CAIL2019-SCM contains 8,964 triplets of cases published by the Supreme People's Court of China. CAIL2019-SCM focuses on detecting similar cases, and the participants are required to check which two cases are more similar in the triplets. There are 711 teams who participated in this year's competition, and the best team has reached a score of 71.88. We have also implemented several baselines to help researchers better understand this task. The dataset and more details can be found from https://githu…
We investigate shared language between U.S. Supreme Court majority opinions and interest groups' corresponding amicus briefs. Specifically, we evaluate whether language that originated in an amicus brief acquired legal precedent status by being cited in the Court's opinion. Using plagiarism detection software, automated querying of a large legal database, and manual analysis, we establish seven instances where interest group amici were able to formulate constitutional case law, setting binding legal precedent. We discuss several such instances for their implications in the Supreme Court's crea…
The CIFAR Synthetic Evidence Corpus for Detecting AI-Generated Evidence
University of Toronto（多伦多大学）；University of Waterloo（滑铁卢大学）；University of British Columbia（不列颠哥伦比亚大学）；Vector Institute（向量研究所）；University of Ottawa（渥太华大学）
Artificial Intelligence techniques are already popular and important in the legal domain. We extract legal indicators from judicial judgment to decrease the asymmetry of information of the legal system and the access-to-justice gap. We use NLP methods to extract interesting entities/data from judgments to construct networks of lawyers and judgments. We propose metrics to rank lawyers based on their experience, wins/loss ratio and their importance in the network of lawyers. We also perform community detection in the network of judgments and propose metrics to represent the difficulty of cases c…
MIT Media Lab（麻省理工学院媒体实验室）；Harvard Law School（哈佛法学院）
This paper demonstrate how NLP can be used to address an unmet need of the legal community and increase access to justice. The paper introduces Legal Precedent Prediction (LPP), the task of predicting relevant passages from precedential court decisions given the context of a legal argument. To this end, the paper showcases a BERT model, trained on 530,000 examples of legal arguments made by U.S. federal judges, to predict relevant passages from precedential court decisions given the context of a legal argument. In 96% of unseen test examples the correct target passage is among the top-10 predi…
Legal Prompt Engineering (LPE) or Legal Prompting is a process to guide and assist a large language model (LLM) with performing a natural legal language processing (NLLP) skill. Our goal is to use LPE with LLMs over long legal documents for the Legal Judgement Prediction (LJP) task. We investigate the performance of zero-shot LPE for given facts in case-texts from the European Court of Human Rights (in English) and the Federal Supreme Court of Switzerland (in German, French and Italian). Our results show that zero-shot LPE is better compared to the baselines, but it still falls short compared …
The Brazilian Supreme Court receives tens of thousands of cases each semester. Court employees spend thousands of hours to execute the initial analysis and classification of those cases -- which takes effort away from posterior, more complex stages of the case management workflow. In this paper, we explore multimodal classification of documents from Brazil's Supreme Court. We train and evaluate our methods on a novel multimodal dataset of 6,510 lawsuits (339,478 pages) with manual annotation assigning each page to one of six classes. Each lawsuit is an ordered sequence of pages, which are stor…
Automatic Construction of a Legal Citation Graph from 100 Million Ukrainian Court Decisions: Large-Scale Extraction, Topological Analysis, and Ontology-Driven Clustering
University of Bucharest（布加勒斯特大学）；University of Wolverhampton（伍尔弗汉普顿大学）；Harvard Medical School（哈佛医学院）；Saarland University（萨尔大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）
In this paper, we investigate the application of text classification methods to support law professionals. We present several experiments applying machine learning techniques to predict with high accuracy the ruling of the French Supreme Court and the law area to which a case belongs to. We also investigate the influence of the time period in which a ruling was made on the form of the case description and the extent to which we need to mask information in a full case ruling to automatically obtain training and test data that resembles case descriptions. We developed a mean probability ensemble…
IIT Kharagpur（印度理工学院卡哈拉格普尔分校）；L3S Research Center（L3S研究中心）
Automatic summarization of legal case documents is an important and practical challenge. Apart from many domain-independent text summarization algorithms that can be used for this purpose, several algorithms have been developed specifically for summarizing legal case documents. However, most of the existing algorithms do not systematically incorporate domain knowledge that specifies what information should ideally be present in a legal case document summary. To address this gap, we propose an unsupervised summarization algorithm DELSumm which is designed to systematically incorporate guideline…
Tsinghua University（清华大学）；Peking University（北京大学）；Chinese Academy of Sciences（中国科学院）；China Justice Big Data Institute（中国司法大数据研究院）；Supreme People’s Court（最高人民法院）
In this paper, we introduce the \textbf{C}hinese \textbf{AI} and \textbf{L}aw challenge dataset (CAIL2018), the first large-scale Chinese legal dataset for judgment prediction. \dataset contains more than $2.6$ million criminal cases published by the Supreme People's Court of China, which are several times larger than other datasets in existing works on judgment prediction. Moreover, the annotations of judgment results are more detailed and rich. It consists of applicable law articles, charges, and prison terms, which are expected to be inferred according to the fact descriptions of cases. For…
