Hobson, J.B. and Slee, D. 1993 Rules, cases and networks in a legal domain, Law, Computers and Artificial Intelligence 2(2),119-134. Well, your liberator is here, and it comes in the form of the neural network. It`s a technology that allows searches to find highly relevant results, even if the results don`t even contain one of the search terms. Without a doubt, keyword research is powerful. It has radically changed the way lawyers and lawyers work. Over the years, research scientists have improved it with Boolean and natural language queries. Oskamp, A., Walker, R.F., Schrickx, J.A., and van den Berg, P.H. 1989. PROLEXS divise and rule: A legal application. In Proceedings of the Second International Conference on Artificial Intelligence and Law, Vancouver: ACM Press, pp. 54-62.
Kennedy, D. 1991. Eine Semiotik der juristische Argumentation. Syracuse Law Review 42, 75. Thousands of years ago, Scripture changed the way laws were communicated. Artificial neural networks could be the next step in making the law accessible to all. Both strategies are not intended to replace humans, but to support human intelligence. Llewellyn, K.N. 1940. The normative, the legal and the law-jobs: The problem of legal method, Yale Law Journal 49, 1355 Seaton also offered a fascinating glimpse into an early prototype to which Casetext gave him access. The prototype uses an approach called fusion in set-top boxes and combines neural network search with neural network synthesis, allowing for a much more robust form of questions and answers against case law and litigation. Arredondo then advocated neural networks, followed by Reents and Seaton to provide examples of innovative ways they have used these research tools in their endeavors.
Philipps, L. 1991. Distribution of damage in car accidents through the use of neural networks, Cardozo Law Review 13, 987-1000. The development of information technology has allowed us to revolutionize the business world. Economics, finance, health care and law. With the development of the digital economy, the legal profession has become an environment conducive to the active use of new information technologies. Lawyers and business economists will face the fact that their activities will be significantly reduced, as advances in artificial intelligence will have a significant impact on the labor market. The Liquid Legal Institute is studying the theory that an artificial neural network ecosystem could be an ideal predictable legal form. Like its biological model, an artificial neural network allows for the flexible storage and binding of information at different levels and categories. Warner, D.R. 1990.
The role of neural networks in the development of legal machines. Rutgers Computer and Technology Law Journal 16, 129-144. Künstliche Intelligenz für das Geschäft mit juristischen Vorlagen. The new solution. « Ten years ago, the high demand for cheap legal services created a new type of market made up of companies specializing in the sale of legal models. Today, the largest companies in this market such as « US Legal », « Rocket Lawyer », « LegalZoom » sell models for more than 100 million US dollars each year. During the founding of this Hobson, J.B. and Slee, D. 1994 Indexing the Theft Act 1968 for case based reasoning and artificial neural networks.
In Proceedings of the Fourth National Conference on Law, Computers and Artificial Intelligence, unnumbered additions, Exeter: Exeter University Centre for Legal Interdisciplinary Development. Thagard, p. 1991. Cardozo Law Review 13, 1001-1004. During her remarks, Samantha Seaton put Allsearch`s power in historical context. In the « bad old days, » lawyers had to gather and label hundreds of documents, exhibits, and statements, organized into huge printed files. The brains of the avocados were neural networks and they were only supported by post-its and binder tabs. Seaton described being an employee who tried to memorize all important documents and statements in the weeks leading up to the trial. When the need to charge a witness suddenly arose, lawyers must have hoped that someone had marked the relevant documents in an easy-to-find manner. Seaton compared this with the power of neural networks, describing how loading 39 days of testimony into AllSearch and introducing them into the courtroom allowed for a quick « on the fly » search for conflicting evidence.
The application of neural networks to speech involves setting up simple language tasks (for humans) such as « guessing the missing word ». * The neural network starts in a random state and its assumptions are ridiculously wrong. But through a massive amount of trial and error and self-correction, the neural network « trains » to more accurately predict missing words. In the process, neural networks become quite « intelligent » about language for reasons that are still a mystery to some (or « black box »). Smart enough to have, in Arredondo`s words, « a profound and immediate impact on the legal profession. » Along this spectrum, symbolic AI tools are often better at searching for specific legal terms and concepts, as legal terms are unambiguous, while neural networks may be better at searching for facts that include more ambiguous concepts such as mood or actions, Riehl said. These issues are at the forefront of current legal debates. In a high-profile U.S. Supreme Court case on gerrymandering,3 machine learning was cited in dissent as a concern. This is not surprising since evidence of Markov chain Monte Carlo algorithms4 has been presented to the lower courts, which share this property of not giving the human operator a detailed explanation of how the entered data affects the results.
This is changing the way people get legal services online. « We don`t stop to express our thanks to the companies that deal with the online sale of document templates. They have come a long way starting from scratch and creating the systems and rules to work in a new market. Their experience and financial results allow us. In some cases, it may even be necessary to develop additional technologies to track the information flowing through the neural network and study these hidden layers8. Many of the most advanced machine learning techniques rely on artificial neural networks that allow systems to « learn » tasks from examples without being programmed specifically for those tasks. Hunter, D. Out of their heads: legal theory in neural networks. Artificial Intelligence and Law 7, 129-151 (1999).
doi.org/10.1023/A:1008301122056 From a mathematical point of view, modern neural networks almost always contain so-called « hidden layers » that process information between the input and output of a neural network system. Nodes in hidden layers are not assigned a specific task or weight by a human programmer, and there is usually no direct way to know how information is processed there. Birmingham, R. 1992. Eine Studie nach Cardozo: De Cicco v. Schweizer, Uncooperative Games and Neural Computing, University of Miami Law Review 47, 121-145. Training a neural network for legal research is similar, Arredondo said — you just let him keep playing the game by giving him sentences with missing words and letting him learn to fill in the corresponding word based on context. Ashley, K.D. 1992. Case-based reasoning and its implications for legal expert systems, Artificial Intelligence and Law 1(2), 113-208.
In his speech, Arredondo called neural networks « one of the greatest leaps in the history of research. » The decisive factor is the balance of these connections. Balancing interests is a human skill because it requires semantic understanding. An artificial neural network cannot perform this ability. But neural networks can reflect this predictably. In plain language, most of today`s machine learning techniques rely on methods that work in such a way that some of what happens is not known to human operators. For this reason, systems that incorporate such methods will lead to new legal challenges for lawyers. Bench-Capon, T.J.M. 1993. Neuronale Netze und offene Textur. In Proceedings of the Fourth International Conference on Artificial Intelligence and Law. Amsterdam: ACM Press, pp.
292-297. Rose, D.E. and Belew, R.K. 1991. A connectionist and symbolic hybrid for improving legal research, International Journal of Man-Machine Studies 35(1), 1-33. One of the most exciting panels at ILTA`s 2022 conference (What is Natural Language Processing and How Can I Use It?) revealed dramatic breakthroughs in natural language processing (NLP) neural network technology. Pablo Arredondo, co-founder/chief innovation officer of Casetext, offered an extremely passionate tutorial on the history of AI over the centuries. The introduction of business search systems such as Lexis and Westlaw in the 1980s radically changed legal research by making cases and laws searchable by keywords. Arredondo enthusiastically announced that neural network technology can now « free search from the prison of keywords. » An essential feature of neural network technology is that it trains effectively.
You`ve probably heard of the gaming AI programs developed by Google`s DeepMind, starting with the original AlphaGo – which was to be trained on thousands of human games – to AlphaGo Zero – which was trained only on the basic rules of the game, without examples – and then on AlphaZero – which received no training and mastered three different games in three days.
