Law Intelligence Research Articles

Beyond the Courts: Artificial Intelligence as a Catalyst for Change in Justice Administration

In the context of technological advances, the concept of digital justice is emerging, an extra-judicial sphere that uses technologies such as artificial intelligence to address controversial situations through physical assistants or even robots, if the litigant so wishes. Thus, while AI is effective in resolving simple disputes without human intervention, even UNESCO warns against its exclusivity in more complicated cases. From this perspective, through a qualitative approach and literature review, this research focused on examining the benefits and limitations of AI in the administration of justice. The review of the academic literature reveals that this technology facilitates the functions performed by judges and lawyers in controversial situations. The studies, however, point to the importance of using human intelligence in law making and more general judicial processes. In conclusion, AI improves the efficiency of the administration of justice; however, its place in judicial operations and process should only be complementary to the use of human intervention. This action maintains fairness and considers the ethical point at stake, reaffirms the existence of some irreplaceable human capabilities in the judicial process.

Prediction of Turkish Constitutional Court Decisions with Explainable Artificial Intelligence

Using artificial intelligence in law is a topic that has attracted attention in recent years. This study aims to classify the case decisions taken by the Constitutional Court of the Republic of Turkey. For this purpose, open-access data published by the Constitutional Court of the Republic of Turkey on the website of the Decisions Information Bank were used in this research. KNN (K-Nearest Neighbors Algorithm), SVM (Support Vector Machine), DT (Decision Tree), RF (Random Forest), and XGBoost (Extreme Gradient Boosting) machine learning (ML) algorithms are used. Precision, Recall, F1-Score, and Accuracy metrics were used to compare the results of these models. As a result of the evaluation showed that the XGBoost model gave the best results with 93.84% Accuracy, 93% Precision, 93% Recall, and 93% F1-Score. It is important that the model result is not only good but also transparent and interpretable. Therefore, in this article, using the SHAP (SHapley Additive exPlanations) method, one of the explainable artificial intelligence techniques, the features that affect the classification of case results are explained. The study is the first study carried out in our country to use explainable artificial intelligence techniques in predicting court decisions in the Republic of Turkey with artificial intelligence.

Discussion on the Reform of Higher Legal Education in China Based on the Application and Limitation of Artificial Intelligence in Law Represented by ChatGPT

ChatGPT, as a representative of generative artificial intelligence, is a disruptive innovation in the field of AI. With its powerful natural language processing technology, ChatGPT has been widely used in the legal profession, providing strong support for the legal industry. As ChatGPT and other AI technologies continue to evolve and become more widely adopted, they will undoubtedly impact the legal industry and current higher education in Chinese law. Based on this, we will explore the challenges and opportunities facing Chinese legal education in the era of artificial intelligence, focusing on the applications and limitations of generative AI such as ChatGPT in the legal profession.

The Era of Artificial Intelligence in Law: Brazil in a Global Context

The Era of Artificial Intelligence in Law: Brazil in a Global Context

Problems of Legal Regulation as in the Case of the Concept of “Artificial Intelligence”

Numerous uses of the phrase “artificial intelligence” both in science and in legal regulation makes it impossible to carelessly and arbitrarily use this category when regulating public relations. It is this circumstance that forces us to address the problems of the conceptual and categorical apparatus and legal regulation of this category. In the paper, the author analyzes the existing approaches to the concept of artificial intelligence presented in the legal literature. The author shows that the genesis of any concept in law and outside of it is primarily related to our knowledge of the object, and concludes that it is impossible to reason about artificial intelligence in law until it is clearly defined in the technical fields of science.The author concludes that the incorporation of artificial intelligence into the ontology of law is possible within the framework of two main approaches. The conceptualist approach involves understanding the properties and knowledge about artificial intelligence as an object of the real world and the subsequent derivation of a legal definition based on this knowledge about the object. The regulatory approach is attributed to the fact that the legislator will assign artificial intelligence a certain legal status regardless of its actual properties and characteristics (by analogy with the assignment of real estate status to aircraft and ships). At the same time, without defining the approach, without analyzing the properties and characteristics of artificial intelligence as a technical and cultural phenomenon, discussions about preventive regulation are, in the author’s opinion, unfounded and not based on either science or law.

Unsupervised law article mining based on deep pre-trained language representation models with application to the Italian civil code

Modeling law search and retrieval as prediction problems has recently emerged as a predominant approach in law intelligence. Focusing on the law article retrieval task, we present a deep learning framework named LamBERTa, which is designed for civil-law codes, and specifically trained on the Italian civil code. To our knowledge, this is the first study proposing an advanced approach to law article prediction for the Italian legal system based on a BERT (Bidirectional Encoder Representations from Transformers) learning framework, which has recently attracted increased attention among deep learning approaches, showing outstanding effectiveness in several natural language processing and learning tasks. We define LamBERTa models by fine-tuning an Italian pre-trained BERT on the Italian civil code or its portions, for law article retrieval as a classification task. One key aspect of our LamBERTa framework is that we conceived it to address an extreme classification scenario, which is characterized by a high number of classes, the few-shot learning problem, and the lack of test query benchmarks for Italian legal prediction tasks. To solve such issues, we define different methods for the unsupervised labeling of the law articles, which can in principle be applied to any law article code system. We provide insights into the explainability and interpretability of our LamBERTa models, and we present an extensive experimental analysis over query sets of different type, for single-label as well as multi-label evaluation tasks. Empirical evidence has shown the effectiveness of LamBERTa, and also its superiority against widely used deep-learning text classifiers and a few-shot learner conceived for an attribute-aware prediction task.

Hybrid K-Nearest Neighbour and Particle Swarm Optimization Technique for Divorce Classification

Judgment is the ability to make a considered decision by an evolution of knowledge. With the increasing trend of applications of artificial intelligence in law, divorce prediction has become the centre of research. Divorce classification and divorce factors determination are two of the most important matters in societies. Developing an effective technique is essential to prevent communities from collapsing. The traditional techniques of artificial intelligence play a major role in classifying divorce cases. Feature selection is a powerful pre-processing method used for data classification problems. Most previous studies on divorce classification focused on heuristic feature selection methods to determine the main factors behind divorce. These heuristic methods are considered the greedy strategy which does not produce an optimal solution. In this research, a new hybrid swarm intelligence technique was proposed using particle swarm optimisation for feature selection and the K-nearest neighbour algorithm for classification. Specifically, the proposed hybrid classifier can be used in real divorce applications where judges in their investigations can identify the factors that lead to the applications. For the experiment, five classifiers were used for performance analysis. The proposed technique was successfully applied and showed that the performance is better than the existing classifiers, namely naive Bayes, support vector machine, artificial neural network, repeated incremental pruning to pro-duce error reduction, and decision stump. Therefore, the proposed classification model is a more suitable technique for divorce classification than other artificial intelligence techniques.

Empirical Research on the Application of Computer Artificial Intelligence in Law

This article uses empirical research methods to explore the status of the application of artificial intelligence in Chinese law. It is found that although the application of artificial intelligence in China’s law has improved, it has also fallen into a dilemma. Its dilemma is mainly manifested in: inadequate deep learning and lack of algorithm transparency. On this basis, this article concludes that the application level of Chinese legal artificial intelligence is relatively elementary. Therefore, we should appropriately reduce the expectation of legal artificial intelligence and reconstruct the conclusion of the development system of legal artificial intelligence.

Understanding the Benefits, Demerits and Criticisms of the Revolution of Computational Analysis and Artificial Intelligence in Law

Computational law is an emerging branch of legal studies and information concerned with the mechanization of legal reasoning. Machine learning can be described a technique by which an automated response is generated based on input data. Legal experts spend lot of time dealing with complicated legal tasks that require effective analysis, reasoning and decision making. Machine learning could be said to replicate human behavior in this sense. This paper debates the benefits, limitations and implications of computational analysis and artificial intelligence in representations of the law.

The Corpus of Artificial Intelligence in Law

The Corpus of Artificial Intelligence in Law

Towards a Real Reconciliation of Centralised and Decentralised Approach in Legal Regulation: Application of an AI-Toolkit to a Problem of Jurisprudence

We present results from the implementation of an approach and a toolkit overcoming the limitations of the existing approaches by which law creates mechanisms for regulating socio-economic interests. In the centralized approach the rules come down from the rulers to the ruled ones. That limits the range of interested individuals to establish and implement regulatory mechanisms. In the decentralized approach, if necessary, rulers delegate certain individuals to create rules ad hoc, expanding the range of subjects of decision. This presupposes possibility of power abuse and take conflicting decisions in analogous cases.These disadvantages have been identified back in the ancient Rome. For their overcoming the systems of ius civile and ius honorarium were combined (II B.C.-I A.C.). Such attempts continue, for example within the EU law. A model that overcomes these disadvantages hasn`t been applied so far, because optimal and socially-efficient combination of centralized and decentralized approaches presupposes specific mechanisms. Their establishment and implementation should cover all interested individuals by coordinating and integrating the partial decisions, guaranteeing an optimal adaptability of the society towards the environment. Such mechanisms are achievable by the application of a cognitive approach, methodology and the G-space architecture for identifying inaccurately or mistakenly formulated motivational mechanisms in legislation. We describe how this set of cognitive instruments could be used to correct mistakes in existing legislation and design new, subject to ex-ante defined purposes. We show that the approach, methodology and G-space architecture extend the boundaries of the inter-disciplinary area of Artificial Intelligence in Law.

Review of Argumentation methods for artificial intelligence in law by Douglas Walton, Springer-Verlag Berlin and Heidelberg GmbH & Co. K, 2005

Review of Argumentation methods for artificial intelligence in law by Douglas Walton, Springer-Verlag Berlin and Heidelberg GmbH & Co. K, 2005