Adaptive Retrieval Method of Legal Information based on Artificial Intelligence

2020 ◽  
Author(s):  
Guiying Yang
Keyword(s):  
Artificial Intelligence ◽  
Retrieval Method ◽  
Legal Information ◽  
Adaptive Retrieval

Artificial Intelligence in Legal Practice: Exploring Theoretical Frameworks for Algorithmic Literacy in the Legal Information Profession

2021 ◽  
Vol 21 (2) ◽  
pp. 97-117
Author(s):  
Dominique Garingan ◽  
Alison Jane Pickard
Keyword(s):  
Artificial Intelligence ◽  
Computer Literacy ◽  
Intelligent Systems ◽  
Technological Literacy ◽  
Legal Practice ◽  
Theoretical Frameworks ◽  
User Experiences ◽  
Legal Information ◽  
Literacy Initiatives ◽  
Academic Settings

AbstractIn response to evolving legal technologies, this article by Dominique Garingan and Alison Jane Pickard explores the concept of algorithmic literacy, a technological literacy which facilitates metacognitive practices surrounding the use of artificially intelligent systems and the principles that shape ethical and responsible user experiences. This article examines the extent to which existing information, digital, and computer literacy frameworks and professional competency standards ground algorithmic literacy. It proceeds to identify various elements of algorithmic literacy within existing literature, provide examples of algorithmic literacy initiatives in academic and non-academic settings, and explore the need for an algorithmic literacy framework to ground algorithmic literacy initiatives within the legal information profession.

ARTIFICIAL INTELLIGENCE AND "PREDICTED JUSTICE": FOREIGN EXPERIENCE

Lex Russica ◽  
2019 ◽  
pp. 79-87
Author(s):  
P. N. Biryukov
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Relevant Information ◽  
Final Decision ◽  
Legal Information ◽  
The Right ◽  
Task Interpretation ◽  
Short Time ◽  
Analytical Tools

The paper deals with the problems of application of artificial intelligence (AI) in the field of justice. Present day environment facilitates the use of AI in law. Technology has entered the market. As a result, "predicted justice" has become possible. Once an overview of the possible future process is obtained, it is easier for the professional to complete the task-interpretation and final decision-making (negotiations, litigation). It will take a lot of work to bring AI up to this standard. Legal information should be structured to make it not only readable, but also effective for decision-making. "Predicted justice" can help both the parties to the case and the judges in structuring information, and students and teachers seeking relevant information. The development of information technology has led to increased opportunities for "predicted justice" programs. They take advantage of new digital tools. The focus is on two advantages of the programs: a) improving the quality of services provided; b) simultaneously monitoring the operational costs of the justice system. "Predicted justice" provides algorithms for analyzing a huge number of situations in a short time, allowing you to predict the outcome of a dispute or at least assess the chances of success. It helps: choose the right way of defense, the most suitable arguments, estimate the expected amount of compensation, etc. Thus, it is not about justice itself, but only about analytical tools that would make it possible to predict future decisions in disputes similar to those that have been analyzed.

Lions and Librarians: a Plea for the Intelligent Conservation of an Unnecessarily Endangered Species

2019 ◽  
Vol 19 (02) ◽  
pp. 88-91 ◽  
Author(s):  
Daniel Greenberg
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Endangered Species ◽  
Legal Services ◽  
Legal Information ◽  
Vital Importance ◽  
Technological Advances ◽  
Wide Range ◽  
Information Professional

AbstractThe role of the law librarian or legal information professional is thought by some to have been diminished significantly by technological advances which provide instant access to an enormous range of materials direct to individual users at their desks. The reality is that the wide range of instantly accessible materials makes the experience and knowledge of the information professional more important, not less; and imminently expected advances in machine learning and artificial intelligence are likely to confirm the vital importance of the legal information professional at the centre of legal services.

scholarly journals References to Artificial Intelligence in Canada's Court Cases

2020 ◽  
Vol 20 (1) ◽  
pp. 39-46 ◽  
Author(s):  
Channarong Intahchomphoo ◽  
André Vellino ◽  
Odd Erik Gundersen ◽  
Christian Tschirhart ◽  
Eslam Shaaban
Keyword(s):  
Artificial Intelligence ◽  
Information Needs ◽  
Federal Courts ◽  
Legal Information ◽  
Reference Services ◽  
Legal Studies ◽  
New Information ◽  
Legal Contexts ◽  
Government Records ◽  
Internet Law

AbstractArtificial intelligence (AI) is a widely discussed topic in many fields including law. Legal studies scholars, particularly in the domain of technology and internet law, have expressed their hopes and concerns regarding AI. This project aims to study how Canada's courts have referred to AI, given the importance of the reasonings of justices to the policy makers who determine society's rules for the usage of AI in the future. Decisions from all levels of both Canada's provincial and federal courts are used as the data sources for this research. The findings indicate that there are four legal contexts in which AI has been referred to in the Canadian caselaw including: legal research, investment tax credits, trademarks and access to government records. In this article the authors use these findings to make suggestions for legal information management professionals on how to develop collections and reference services that are in line with the new information needs of their users regarding AI and the rule of law.

scholarly journals Artificial Intelligence Models for Crime Prediction in Urban Spaces

2020 ◽  
Vol 8 (1) ◽  
pp. 1-13
Author(s):  
Ana Laura Lira Cortes ◽  
Carlos Fuentes Silva
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Urban Areas ◽  
Urban Context ◽  
Criminal Records ◽  
Legal Information ◽  
Legal Advice ◽  
Intelligence Services ◽  
Artificial Intelligence Models ◽  
The Government

This work presents research based on evidence with neural networks for the development of predictive crime models, finding the data sets used are focused on historical crime data, crime classification, types of theft at different scales of space and time, counting crime and conflict points in urban areas. Among some results, 81% precision is observed in the prediction of the Neural Network algorithm and ranges in the prediction of crime occurrence at a space-time point between 75% and 90% using LSTM (Long-ShortSpace-Time). It is also observed in this review, that in the field of justice, systems based on intelligent technologies have been incorporated, to carry out activities such as legal advice, prediction and decisionmaking, national and international cooperation in the fight against crime, police and intelligence services, control systems with facial recognition, search and processing of legal information, predictive surveillance, the definition of criminal models under the criteria of criminal records, history of incidents in different regions of the city, location of the police force, established businesses, etc., that is, they make predictions in the urban context of public security and justice. Finally, the ethical considerations and principles related to predictive developments based on artificial intelligence are presented, which seek to guarantee aspects such as privacy, privacy and the impartiality of the algorithms, as well as avoid the processing of data under biases or distinctions. Therefore, it is concluded that the scenario for the development, research, and operation of predictive crime solutions with neural networks and artificial intelligence in urban contexts, is viable and necessary in Mexico, representing an innovative and effective alternative that contributes to the attention of insecurity, since according to the indices of intentional homicides, the crime rates of organized crime and violence with firearms, according to statistics from INEGI, the Global Peace Index and the Government of Mexico, remain in increase.

Artificial Intelligence and Race: a Systematic Review

2020 ◽  
Vol 20 (2) ◽  
pp. 74-84 ◽  
Author(s):  
Channarong Intahchomphoo ◽  
Odd Erik Gundersen
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Information Management ◽  
Web Of Science ◽  
Lessons Learned ◽  
Racial Groups ◽  
Legal Information ◽  
Unequal Opportunities ◽  
Facial Images ◽  
Academic Search

AbstractThis paper examines peer-reviewed publications to learn about the relationships between artificial intelligence (AI) and the human race. For this systematic review, papers were collected from three academic databases: Scopus, Web of Science, and Academic Search Complete. From 1,222 papers reviewed, 36 papers were included. The findings indicate that there are four relationships between AI and race (i). AI causes unequal opportunities for people from certain racial groups, (ii). AI helps to detect racial discrimination, (iii). AI is applied to study health conditions of specific racial population groups, and (iv). AI is used to study demographics and facial images of people from different racial backgrounds. To widen the knowledge related to AI and race, all four finding categories in this review included supplementary studies as lessons learned for legal information management research. The authors, Channarong Intahchomphoo and Odd Erik Gundersen, use these findings to discuss how AI could impact libraries and how legal information management professionals might have to cope with the problem of biased AI.

scholarly journals Law, Artificial Intelligence, and Natural Language Processing: A Funny Thing Happened on the Way to My Search Results

2020 ◽  
Author(s):  
Paul Douglas Callister
Keyword(s):  
Artificial Intelligence ◽  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Science Literature ◽  
Legal Research ◽  
Legal Information ◽  
Search Results ◽  
The Law

Renowned legal educator Roscoe Pound stated, “Law must be stable and yet it cannot stand still.” Yet, as Susan Nevelow Mart has demonstrated in a seminal article that the different online research services (Westlaw, Lexis Advance, Fastcase, Google Scholar, Ravel and Casetext) produce significantly different results when researching case law. Furthermore, a recent study of 325 federal courts of appeals decisions, revealed that only 16% of the cases cited in appellate briefs make it into the courts’ opinions. This does not exactly inspire confidence in legal research or its tools to maintain stability of the law. As Robert Berring foresaw, “The world of established sources and sets of law book that has been so stable at to seem inevitable suddenly has vanished. The familiar set of printed case reporters, citators, and second sources that were the core of legal research are being minimized before our eyes.”In this article I focus on Artificial Intelligence (AI) and natural language processing with respect to searching. My article will proceeds as follows. To understand how effective natural language processing is in current legal research, I go about building a model of a legal information retrieval system that incorporates natural language processing. I have had to build my own model because we do not know very much about how the proprietary systems of Westlaw, Lexis, Bloomberg, Fastcase and Casetext work. However, there are descriptions in information science literature and on the Internet of how systems with advanced programing techniques actually work or could work. Next, I compare such systems with the features and search results produced by the major vendors to illustrate the probable use of natural language processing, similar to the models. In addition, the use of word prediction or type ahead techniques in the major research services are studied--particularly, how such techniques can be used to bring secondary resources to the forefront of a search. Finally, I explore how the knowledge gained may help us to better instruct law students and attorneys in the use of the major legal information retrieval systems.My conclusion is that the adeptness of natural language processing is uneven among the various vendors and that what we receive in search results from such systems varies widely depending on a host of unknown variables. Natural language processing has introduced uncertainty to the law. We are a long way from AI systems that understand, let alone search, legal texts in a stable and consistent way.

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