Artificial Intelligence and Law最新文献

We 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.

This article discusses the desirability and feasibility of modeling precedents with multiple interpretations within factor-based models of precedential constraint. The main idea is that allowing multiple reasonable interpretations of cases and modeling precedential constraint as a function of what all reasonable interpretations compel may be advantageous. The article explains the potential benefits of extending the models in this way with a focus on incorporating a theory of vertical precedent in U.S. federal appellate courts. It also considers the costs of extending the models in this way, such as the significant increase in the functional size of the case base and the need to provide some kind of ordering on interpretations to select a “best” interpretation. Finally, the article suggests partially incorporating multiple interpretations of dimensions as a realistic starting point for incorporating interpretations generally, and shows how doing so can help address difficulties with dimensions. The conclusion remarks on the use of interpretations to deal with inconsistent precedents.

Court rulings are among the most important documents in all legal systems. This article describes a study in which natural language processing is used for the automatic characterization of Spanish judgments that deal with the physical custody (joint or individual) of minors. The model was trained to identify a set of elements: the type of custody requested by the plaintiff, the type of custody decided on by the court, and eight of the most commonly used arguments in this type of judgment. Two jurists independently annotated more than 3000 judgments, which were used to train a model based on transformers. The main difficulties encountered in this task were the complexity of the judicial language and the need to work with appellate court rulings that have a more complicated structure than decisions at first instance. For the complete court rulings, the F1 score of the inter-annotator agreement ranged from 0.60 to 0.86 and the Kappa index from 0.33 to 0.73. The F1 score of the agreement between the model and the annotators ranged from 0.66 to 0.93 and the Kappa index from 0.57 to 0.80. These results in which the model performance exceeds even the inter-annotator agreement show the high ability of transformers to identify abstract entities in legal texts.

Automated classification of legal documents has been the subject of extensive research in recent years. However, this is still a challenging task for long documents, since it is difficult for a model to identify the most relevant information for classification. In this paper, we propose a two-stage supervised learning approach for the classification of petitions, a type of legal document that requests a court order. The proposed approach is based on a word-level encoder–decoder Seq2Seq deep neural network, such as a Bidirectional Long Short-Term Memory (BiLSTM) or a Bidirectional Encoder Representations from Transformers (BERT) model, and a document-level Support Vector Machine classifier. To address the challenges posed by the lengthy legal documents, the approach introduces a human-in-the-loop approach, whose task is to localize and tag relevant segments of text in the word-level training part, which dramatically reduces the dimension of the document classifier input vector. We performed experiments to validate our approach using a real-world dataset comprised of 270 intermediate petitions, which were carefully annotated by specialists from the 15th civil unit of the State of Alagoas, Brazil. Our results revealed that both BiLSTM and BERT-Convolutional Neural Networks variants achieved an accuracy of up to 95.49%, and also outperformed baseline classifiers based on the Term Frequency–Inverse Document Frequency test vectorizer. The proposed approach is currently being utilized to automate the aforementioned justice unit, thereby increasing its efficiency in handling repetitive tasks.

Computational models of legal precedent-based reasoning developed in AI and Law are typically based on the simplifying assumption that the background set of precedent cases is consistent. Besides being unrealistic in the legal domain, this assumption is problematic for recent promising applications of these models to the development of explainable AI methods. In this paper I explore a model of legal precedent-based reasoning that, unlike existing models, does not rely on the assumption that the background set of precedent cases is consistent. The model is a generalization of the reason model of precedential constraint. I first show that the model supports an interesting deontic logic, where consistent obligations can be derived from inconsistent case bases. I then provide an explanation of this surprising result by proposing a reformulation of the model in terms of cases that support a new potential decision and cases that conflict with it. Finally, I show that the reformulation of the model allows us to verify that inconsistent case bases do not make verification that a decision is permissible substantially more complex than consistent case bases and to introduce intuitive criteria to compare different permissible decisions.

Freedom of information laws promote transparency by permitting individuals and organizations to obtain government documents. However, exemptions from disclosure are necessary to protect privacy and to permit government officials to deliberate freely. Deliberative language is often the most challenging and burdensome exemption to detect, leading to high processing costs and delays in responding to open-records requests. This paper describes a novel deliberative-language detection model trained on a new annotated training set. The deliberative-language detection model is a component of a decision-support system for open-records requests under the US Freedom of Information Act, the FOIA Assistant, that ingests documents responsive to an open-records requests, suggests passages likely to be subject to deliberative language, privacy, or other exemptions, and assists analysts in rapidly redacting suggested passages. The tool’s interface is based on extensive human-factors and usability studies with analysts and is currently in operational testing by multiple US federal agencies.