Teaching Law and Digital Age Legal Practice with an AI and Law Seminar

This article provides a guide and examples for using a seminar on Artificial Intelligence (AI) and Law to teach lessons about legal reasoning and about legal practice in the digital age. Artificial Intelligence and Law is a subfield of AI/ computer science research that focuses on computationally modeling legal reasoning. In at least a few law schools, the AI and Law seminar has regularly taught students fundamental issues about law and legal reasoning by focusing them on the problems these issues pose for scientists attempting to computationally model legal reasoning. AI and Law researchers have designed programs to reason with legal rules, apply legal precedents, predict case outcomes, argue like a legal advocate and visualize legal arguments. The article illustrates some of the pedagogically important lessons that they have learned in the process. As the technology of legal practice catches up with the aspirations of AI and Law researchers, the AI and Law seminar can play a new role in legal education. With advances in such areas as e-discovery, legal information retrieval (IR), and semantic processing of web-based information for electronic contracting, the chances are increasing that, in their legal practices, law students will use, and even depend on, systems that employ AI techniques. As explained in the Article, an AI and Law seminar invites students to think about processes of legal reasoning and legal practice and about how those processes employ information. It teaches how the new digital documents technologies work, what they can and cannot do, how to measure performance, how to evaluate claims about the technologies, and how to be savvy consumers and users of the technologies

Law Schools as Knowledge Centers in the Digital Age

This article explores what it would mean for law schools to be “knowledge centers” in the digital age, and to have this as a central mission. It describes the activities of legal knowledge centers as: (1) focusing on solving real legal problems in society outside of the academy; (2) evaluating the problem-solving effectiveness of the legal knowledge being developed; (3) re-conceptualizing the structures used to represent legal knowledge, the processes through which legal knowledge is created, and the methods used to apply that knowledge; and (4) disseminating legal knowledge in ways that assist its implementation. The Article uses as extended examples of knowledge centers in the digital age the research laboratories in the sciences, and in particular research laboratories in linguistics and information science. It uses numerous examples to suggest how law schools might implement the concept of a knowledge center

Law Schools as Knowledge Centers in the Digital Age

This article explores what it would mean for law schools to be “knowledge centers” in the digital age, and to have this as a central mission. It describes the activities of legal knowledge centers as: (1) focusing on solving real legal problems in society outside of the academy; (2) evaluating the problem-solving effectiveness of the legal knowledge being developed; (3) re-conceptualizing the structures used to represent legal knowledge, the processes through which legal knowledge is created, and the methods used to apply that knowledge; and (4) disseminating legal knowledge in ways that assist its implementation. The Article uses as extended examples of knowledge centers in the digital age the research laboratories in the sciences, and in particular research laboratories in linguistics and information science. It uses numerous examples to suggest how law schools might implement the concept of a knowledge center

An overview of information extraction techniques for legal document analysis and processing

In an Indian law system, different courts publish their legal proceedings every month for future reference of legal experts and common people. Extensive manual labor and time are required to analyze and process the information stored in these lengthy complex legal documents. Automatic legal document processing is the solution to overcome drawbacks of manual processing and will be very helpful to the common man for a better understanding of a legal domain. In this paper, we are exploring the recent advances in the field of legal text processing and provide a comparative analysis of approaches used for it. In this work, we have divided the approaches into three classes NLP based, deep learning-based and, KBP based approaches. We have put special emphasis on the KBP approach as we strongly believe that this approach can handle the complexities of the legal domain well. We finally discuss some of the possible future research directions for legal document analysis and processing

Development and validation of the Personal Values Dictionary:A theory-driven tool for investigating references to basic human values in text

Estimating psychological constructs from natural language has the potential to expand the reach and applicability of personality science. Research on the Big Five has produced methods to reliably assess personality traits from text, but the development of comparable tools for personal values is still in the early stages. Based on the Schwartz theory of basic human values, we developed a dictionary for the automatic assessment of references to personal values in text. To refine and validate the dictionary, we used Facebook updates, blog posts, essays, and book chapters authored by over 180,000 individuals. The results show high reliability for the dictionary and a pattern of correlations between the value types in line with the circumplex structure. We found small to moderate (rs=.1-.4) but consistent correlations between dictionary scores and self-reported scores for 7 out of 10 values. Correlations between the dictionary scores and age, gender, and political orientation of the author and scores for other established dictionaries mostly followed theoretical predictions. The Personal Values Dictionary can be used to assess references to value orientations in textual data, such as tweets, blog posts, or status updates, and will stimulate further research in methods to assess human basic values from text

Machine learning versus knowledge based classification of legal texts

This paper presents results of an experiment in which we used machine learning (ML) techniques to classify sentences in Dutch legislation. These results are compared to the results of a pattern-based classifier. Overall, the ML classifier performs as accurate (>90%) as the pattern based one, but seems to generalize worse to new laws. Given these results, the pattern based approach is to be preferred since its reasons for classification are clear and can be used for further modelling of the content of the sentences