214:
to bundle work together, that era is coming to an end, as clients unbundle legal services and tasks. Moreover, in other cases, automation and technology can change the roles of lawyers, both requiring them to oversee processes and use technology more aggressively as well as doing less of the work that is increasingly managed by computers (think: electronic discovery). The upside is not only greater efficiencies for society, but new possibilities for legal craftsmanship. The emerging craft of lawyering in the New Normal is likely to require lawyers to be both entrepreneurial and fluent with a range of competencies that will enable them to add value for clients. Apropos of the trends noted above, there are emerging opportunities for "legal entrepreneurs" in a range of roles from legal process management to developing technologies to manage legal operations (such as overseeing automated processes) to supporting online dispute resolution processes. In other cases, effective legal training as well as domain specific knowledge (finance, sales, IT, entrepreneurship, human resources, etc.) can form a powerful combination that prepares law school grads for a range of opportunities (business development roles, financial operations roles, HR roles, etc.). In both cases, traditional legal skills alone will not be enough to prepare law students for these roles. But the proper training, which builds on the traditional law school curriculum and goes well beyond it including practical skills, relevant domain knowledge (e.g., accounting), and professional skills (e.g., working in teams), will provide law school students a huge advantage over those with a one-dimensional skill set."
229:"So we're slowly moving toward people being educated in the kind of computational paradigm. Which is good, because the way I see it, computation is going to become central to almost every field. Let's talk about two examples—classic professions: law and medicine. It's funny, when Leibniz was first thinking about computation at the end of the 1600s, the thing he wanted to do was to build a machine that would effectively answer legal questions. It was too early then. But now we’re almost ready, I think, for computational law. Where for example contracts become computational. They explicitly become algorithms that decide what's possible and what's not.You know, some pieces of this have already happened. Like with financial derivatives, like options and futures. In the past these used to just be natural language contracts. But then they got codified and parametrized. So they’re really just algorithms, which of course one can do meta-computations on, which is what has launched a thousand hedge funds, and so on. Well, eventually one's going to be able to make computational all sorts of legal things, from mortgages to tax codes to perhaps even patents. Now to actually achieve that, one has to have ways to represent many aspects of the real world, in all its messiness. Which is what the whole knowledge-based computing of Wolfram|Alpha is about."
367:. The density of connections between nodes, and the sheer number of nodes in some cases, can make the visualization incomprehensible to humans. There are a variety of methods that can be used to reduce the complexity of the displayed information, for example by defining semantic sub-groups within the network, and then representing relationships between these semantic groups, rather than between every node. This allows the visualization to be human readable, but the reduction in complexity can obscure relationships. Despite this limitation, visualization of legal citation networks remains a popular field and practice.
157:
thought processes through computational methods and then apply that capacity to solve legal problems, thus automating and improving legal services via increased efficiency as well as shedding light on the nature of legal reasoning. By the late 1970s, computer science and the affordability of computer technology had progressed enough that the retrieval of "legal data by electronic and mechanical means" had been achieved by machines fitting Mehl's first type and were in common use in
American law firms. During this time, research focused on improving the goals of the early 1970s occurred, with programs like
153:. Mehl believed that the law could by automated by two basic distinct, though not wholly separable, types of machine. These were the "documentary or information machine", which would provide the legal researcher quick access to relevant case precedents and legal scholarship, and the "consultation machine", which would be "capable of answering any question put to it over a vast field of law". The latter type of machine would be able to basically do much of a lawyer's job by simply giving the "exact answer to a problem put to it".
113:. Though broadly said to be concerned with the application of the "methods of science" to the law, these methods were actually of a quite specifically defined scope. Jurimetrics was to be "concerned with such matters as the quantitative analysis of judicial behavior, the application of communication and information theory to legal expression, the use of mathematical logic in law, the retrieval of legal data by electronic and mechanical means, and the formulation of a calculus of legal predictability".
350:. This research has been used to analyze various aspects of the Code, including its size, the density of citations within and between sections of the Code, the type of language used in the Code, and how these features vary over time. This research has been used to provide commentary on the nature of the Code's change over time, which is characterized by an increase in size and in interdependence between sections.
178:
approach, was "hugely influential for the development of computational representations of legislation, showing how logic programming enables intuitively appealing representations that can be directly deployed to generate automatic inferences". In 2021, this work received the
Inaugural CodeX Prize as "one of the first and best-known works in computational law, and one of the most widely cited papers in the field."
193:("AI") techniques have not yet been widely applied to perform legal tasks. Therefore, Gardner, and this review, first describe and define the field, then demonstrate a working model in the domain of contract offer and acceptance." Eight years after the Swansea conference had passed, and still AI and law researchers merely trying to delineate the field could be described by their own kind as "pioneer".
234:
they use a deep-learning algorithm to validate the results from satellite comparison thus saving nearly a million dollars per annum. The perceived success of this operation has led to the
Estonian government moving ahead with proposals to automate, or compute the law of, small claims disputes beneath $ 8,000 especially in contract disputes. The decisions will be appealable to a human judge.
201:
analyze, predict and worry about the potential future of computational law and a new academic field of computational legal studies seems to be now well established. As insight into what such scholars see in the law's future due in part to computational law, here is quote from a recent conference about the "New Normal" for the legal profession:
205:"Over the last 5 years, in the fallout of the Great Recession, the legal profession has entered the era of the New Normal. Notably, a series of forces related to technological change, globalization, and the pressure to do more with less (in both corporate America and law firms) has changed permanently the legal services industry. As
255:. A machine readable code would simplify the analysis of legal code, allowing the rapid construction and analysis of databases, without the need for advanced text processing techniques. A machine executable format would allow the specifics of a case to be input, and would return the decision based on the case.
213:
that it had initially expected old work to return, but came "around to the view that this is the ‘new normal.’"The New Normal provides lawyers with an opportunity to rethink—and reimagine—the role of lawyers in our economy and society. To the extent that law firms enjoyed, or still enjoy, the ability
196:
In the 1990s and early 2000s more progress occurred. Computational research generated insights for law. The First
International Conference on AI and the Law occurred in 1987, but it is in the 1990s and 2000s that the biannual conference began to build up steam and to delve more deeply into the issues
37:
While there are many possible applications of
Computational Law, the primary focus of work in the field today is compliance management, i.e. the development and deployment of computer systems capable of assessing, facilitating, or enforcing compliance with rules and regulations. Some systems of this
220:
Many see perks to oncoming changes brought about by the computational automation of law. For one thing, legal experts have predicted that it will aid legal self-help, especially in the areas of contract formation, enterprise planning, and the prediction of rule changes. For another thing, those with
177:
inference from a specified goal; they ask questions to elicit information from the user; and they produce a suggested answer along with a trace of the supporting legal rules." According to
Prakken and Sartor the representation of the British Nationality Act as a logic program, which introduced this
334:
decisions, compiling a publicly available database of Tax Court decisions, opinions, and citations between the years of 1990 and 2008, and constructing a citation network from this database. Analysis of this network revealed that large sections of the tax code were rarely, if ever, cited, and that
233:
In
Estonia, the government has been spearheading a 'robotic judge' initiative whereby chief data officer Ott Velsberg is implementing elements both adjacent and directly related to computational law. Firstly, inspectors no longer verify the use of hay-field subsidies (that prevent forests) rather
200:
Further, by 2005, a team largely composed of
Stanford computer scientists from the Stanford Logic group had devoted themselves to studying the uses of computational techniques to the law. Computational methods in fact advanced enough that members of the legal profession began in the 2000s to both
156:
By 1970, Mehl's first type of machine, one that would be able to retrieve information, had been accomplished but there seems to have been little consideration of further fruitful intersections between AI and legal research. There were, however, still hopes that computers could model the lawyer's
133:
Today, however, the journal and meaning of jurimetrics seems to have broadened far beyond what would fit under the areas of applications of computers and computational methods to law. Today the journal not only publishes articles on such practices as found in computational law, but has broadened
358:
Visualization of legal code, and of the relationships between various laws and decisions, is also a hot topic in computational law. Visualizations allow both professionals and laypeople to see large-scale relationships and patterns, which may be difficult to see using standard legal analysis or
277:
Machine executable legal code is much less common. Currently, as of 2020, numerous projects are working on systems for producing machine executable legal code, sometimes also through natural language, constrained language or a connection between natural language and executable code similar to
164:
Nonetheless, progress on the second type of machine, one that would more fully automate the law, remained relatively inert. Research into machines that could answer questions in the way that Mehl's consultation machine would picked up somewhat in the late 1970s and 1980s. A 1979 convention in
93:
appears to utilize computational methods. The forms that speculation took are multiple and not all related in ways to readily show closeness to one another. This history will sketch them as they were, attempting to show relationships where they can be found to have existed.
927:
Bench-Capon, Trevor, Michał Araszkiewicz, Kevin Ashley, Katie
Atkinson, Floris Bex, Filipe Borges, Daniele Bourcier et al. "A history of AI and Law in 50 papers: 25 years of the international conference on AI and Law." Artificial Intelligence and Law 20, no. 3 (2012):
314:
algorithms in order to relate cases to one another, as well as the use of various distance metrics to find mathematical relationships between them. These analyses can reveal important overarching patterns and trends in judicial proceedings and the way law is used.
169:, Wales marked the first international effort solely to focus upon applying artificial intelligence research to legal problems in order to "consider how computers can be used to discover and apply the legal norms embedded within the written sources of the law".
322:
majority opinions to build citation networks, and analyzed the patterns in these networks to identify meta-information about individual decisions, such as the importance of the decision, as well as general trends in judicial proceedings, such as the role of
945:>. The citation includes all past conferences and links to their contents. It appears that during the 1990s the number of papers presented, talks given, etc. increased significantly from the first two conferences held respectively in 1987 and 1989.
172:
Considerable progress on the development of the second type of machine was made in the following decade, with the development of a variety of expert systems. According to Thorne McCarty, "these systems all have the following characteristics: They do
33:
concerned with the automation of legal reasoning. What distinguishes
Computational Law systems from other instances of legal technology is their autonomy, i.e. the ability to answer legal questions without additional input from human legal experts.
362:
Legal citation networks lend themselves to visualization, and many citation networks which are analyzed empirically also have sub-sections of the network that are represented visually as a result. However, there are still many technical problems in
69:(e.g. delivery schedules, insurance covenants, real estate transactions, financial agreements). They can be the policies of corporations (e.g. constraints on travel, expenditure reporting, pricing rules). They can even be the rules of
149:, UK, the French jurist Lucien Mehl presented a paper both on the benefits of using computational methods for law and on the potential means to use such methods to automate law for a discussion that included AI luminaries like
197:
involved with work intersecting computational methods, AI, and law. Classes began to be taught to undergraduates on the uses of computational methods to automating, understanding, and obeying the law.
221:
knowledge about computers see the potential for computational law to really fully bloom as eminent. In this vein, it seems that machines like Mehl's second type may come into existence.
209:
put it, firms are cutting back on hiring "in order to increase efficiency, improve profit margins, and reduce client costs." Indeed, in its recently noted cutbacks, Weil Gotshal's
909:
See, e.g., Kades, Eric, "The Laws of Complexity & the Complexity of Laws: The Implications of Computational Complexity Theory for the Law" 49 Rutgers Law Review 403-484 (1997)
1102:
Fowler, J. H., T. R. Johnson, J. F. Spriggs, S. Jeon, and P. J. Wahlbeck. "Network Analysis and the Law: Measuring the Legal Importance of Precedents at the U.S. Supreme Court."
1259:
290:
Many current efforts in computational law are focused on the empirical analysis of legal decisions, and their relation to legislation. These efforts usually make use of
318:
There have been several breakthroughs in the analysis of judicial rulings in recent research on legal citation networks. These analyses have made use of citations in
879:
134:
jurimetrical concerns to mean also things like the use of social science in law or the "policy implications and legislative and administrative control of science".
383:
274:
in the May 2013 mandated that all public government documentation be released in a machine readable format by default, although no specific format was mentioned.
270:, is used by the governments of both the United Kingdom and the Netherlands to encode their laws. In the United States, an executive order issued by President
1179:
Ridi, Niccolò (1 June 2019). "The Shape and Structure of the 'Usable Past': An Empirical Analysis of the Use of Precedent in International Adjudication".
161:
being worked on in order to both bring useful computer technology into the law as practical aids and to help specify the exact nature of legal concepts.
596:
777:
677:
Mechanization of Thought Processes: Proceedings of a Symposium Held at the National Physical Laboratory on 24th, 25th, 26th and 27th November 1958
664:
Mechanization of Thought Processes: Proceedings of a Symposium Held at the National Physical Laboratory on 24th, 25th, 26th and 27th November 1958
638:
Mechanization of Thought Processes: Proceedings of a Symposium Held at the National Physical Laboratory on 24th, 25th, 26th and 27th November 1958
592:. American Bar Association Section of Science & Technology Law and the Center for Law, Science & Innovation, n.d. Web. 26 Apr. 2014. <
991:
405:
Legal Analytics, which combines big data, critical expertise, and intuitive tools to deliver business intelligence and benchmarking solutions.
138:
42:
is a good example. And the potential is particularly significant now due to recent technological advances – including the prevalence of the
436:
maintains a comprehensive online repository of legal information, including legislation at the international, national, and state levels.
443:
Database is a comprehensive database containing detailed information about decisions made by the Supreme Court from 1946 to the present.
1131:
Bommarito, Michael J., Daniel Martin Katz, Jonathan L. Zelner, and James H. Fowler. "Distance Measures for Dynamic Citation Networks."
1304:
Starger, Colin P. (16 April 2012). "Expanding Stare Decisis: The Role of Precedent in the Unfolding Dialectic of Brady v. Maryland".
1235:
Model-Driven Development of Akoma Ntoso Application Profiles - A Conceptual Framework for Model-Based Generation of XML Subschemas
918:
Rissland, E. L., Ashley, K. D., & Loui, R. P. (2003). AI and Law: A fruitful synergy. Artificial Intelligence, 150(1-2), 1-15.
408:
Legal visualizations. Examples include Katz's map of supreme court decisions, Starger's Opinion Lines for the commerce clause and
651:
Computer Science and the Law: Inaugural Lecture of the Professor of Computer Science Delivered at the College on January 25, 1977
1446:
426:
390:
122:, as a forum wherein articles would be published about the applications of techniques such as mathematical logic, engineering,
1009:
1242:
1273:
Starger, Colin P. (30 June 2012). "A Visual Guide to NFIB v. Sebelius: Competing Commerce Clause Opinion Lines 1789-2012".
339:, hobby and business expenses and losses, and general definition of income," were involved the vast majority of disputes.
954:
See, e.g., this syllabus from Stanford for CS 204 Computers and Law. Genesereth, Michael R. "CS 204: Computers and Law."
440:
785:
65:
There are also applications that do not involve governmental laws. The regulations can just as well be the terms of
1332:
740:
Niblett, Bryan. B. Niblett, editor. Computer Science and Law: An Advanced Course. Cambridge University Press, 1980.
460:
86:
896:
Rissland, Edwina. "Artificial Intelligence and Legal Reasoning: A Discussion of the Field and Gardner's Book."
726:
Legal Decisions and Information Systems. Jon Bing and Trygve Harvold. Oslo, Norway: Universitets Forlaget; 1977
89:
and computational law do not seem easily separable, as perhaps most of AI research focusing on the law and its
81:
Speculation about potential benefits to legal practice through applying methods from computational science and
396:, which are standardizations created by legal and technical experts for the electronic exchange of legal data.
1118:
Bommarito, Michael J., and Daniel M. Katz. "A Mathematical Approach to the Study of the United States Code."
713:
Some Speculation about Artificial Intelligence and Legal Reasoning Bruce G. Buchanan and Thomas E. Headrick,
700:
Some Speculation about Artificial Intelligence and Legal Reasoning Bruce G. Buchanan and Thomas E. Headrick,
593:
1023:
1436:
1062:
Hoekstra, R. J.; Boer, A. W. F.; Winkels, R. G. F. (2003). "METAlex: An XML Standard for Legal Documents".
992:
http://computationallegalstudies.com/2014/04/17/the-future-of-law-school-innovation-conference-coloradolaw/
810:
327:
over time. These analyses have been used to predict which cases the Supreme Court will choose to consider.
303:
118:
836:
844:
880:"New CodeX Prize Awarded to Computational Law Pioneers During 9th Annual CodeX FutureLaw Conference"
557:
18 Rocky Mntn. L. Rev. 378 (1945-1946) Does the Law Need a Technological Revolution; Kelso, Louis O.
942:
47:
1165:
Bommarito, Michael J. "Empirical Survey of the Population of US Tax Court Written Decisions, An."
500:
433:
331:
267:
190:
82:
450:
contained detailed information about every Supreme Court decision from 1791 to the near-present.
263:
1317:
1290:
1086:
Executive Order -- Making Open and Machine Readable the New Default for Government Information
1004:
Wolfram, Stephen. "Talking about the Computational Future at SXSW 2013—Stephen Wolfram Blog."
539:
531:
1441:
447:
364:
343:
248:
128:
1010:
http://blog.stephenwolfram.com/2013/03/talking-about-the-computational-future-at-sxsw-2013/
988:"The Future of Law School Innovation (Conference @ColoradoLaw)."Computational Legal Studies
480:
376:
1394:
8:
1414:
1365:
1260:"Visualizing Temporal Patterns in the United States Supreme Court's Network of Citations"
294:, which examines patterns in citations between works. Due to the widespread practice of
861:
835:
Sergot, M.J.; Sadri, F.; Kowalski, R.A.; Kriwaczek, F.; Hammond, P; Cory, H.T. (1986).
802:
347:
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85:
research to automate parts of the law date back at least to the middle 1940s. Further,
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Independently in 1958, at the Conference for the Mechanization of Thought held at the
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1286:
1238:
751:
Reflections on Taxman: An Experiment in Artificial Intelligence and Legal Reasoning,
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475:
291:
174:
55:
30:
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853:
806:
794:
762:
McCarty, L. Thorne. "Artificial Intelligence and Law: How to get there from here."
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33 Minn. L. Rev. 455 (1948-1949) Jurimetrics--The Next Step Forward; Loevinger, Lee
399:
307:
299:
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1205:
Shneiderman, Ben, and Aleks Aris. "Network Visualization by Semantic Substrates."
798:
600:
311:
222:
186:
1149:
Fowler, James H., and Sangick Jeon. "The Authority of Supreme Court Precedent."
515:
126:, etc. to the legal study and development. In 1966, this Journal was renamed as
1049:"CEN MetaLex - Open XML Interchange Format for Legal and Legislative Resources"
402:, which correspond to custom-generated copyright licenses for internet content.
295:
98:
109:
methods to legal problems had been founded by American legal scholars, called
1430:
409:
319:
150:
106:
1192:
335:
other sections of code, such as those that dealt with "divorce, dependents,
959:
679:. London: Her Majesty's Stationery Office, 1959. 755-87. Print. At 768-769.
271:
102:
51:
20:
1071:
937:
See "International Conference on Artificial Intelligence and Law (ICAIL)."
594:
http://www.law.asu.edu/jurimetrics/JurimetricsJournal/AbouttheJournal.aspx
1282:
465:
422:
386:
110:
666:. London: Her Majesty's Stationery Office, 1959. 755-87. Print. At 759.
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252:
142:
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941:. The DBLP Computer Science Bibliography, n.d. Web. 24 Apr. 2014. <
857:
379:
324:
146:
66:
43:
39:
185:(1987), the Harvard academic legal scholar and computer scientist
575:
Loevinger, Lee. "Jurimetrics: The methodology of legal inquiry."
393:
258:
Machine readable legal code is already quite common. METAlex, an
166:
1074:– via University of Amsterdam Digital Academic Repository.
943:
http://www.informatik.uni-trier.de/~LEY/db/conf/icail/index.html
976:
470:
425:
is an online repository of judicial rulings, maintained by the
1333:"Visualizing U.S. Copyright Law Using a Force Directed Graph"
346:, in combination with citation networks, and the analysis of
59:
834:
1360:
1219:
778:"Law and logic: a review from an argumentation perspective"
70:
1380:
1048:
653:. Swansea, Wales: U College of Swansea, 1977. 7-8. Print.
259:
1346:
1207:
IEEE Transactions on Visualization and Computer Graphics
717:, Vol. 23, No. 1 (Nov., 1970), pp. 40-62. At pp. 51-60.
640:. London: Her Majesty's Stationery Office, 1959. Print.
416:
183:
An Artificial Intelligence Approach to Legal Reasoning
116:
These interests led in 1959 to the founding a journal,
1133:
Physica A: Statistical Mechanics and Its Applications
1120:
Physica A: Statistical Mechanics and Its Applications
1064:
Proceedings of the XML Europe Conference, London (UK)
189:
wrote that "She plays, in part, the role of pioneer;
1024:"Can AI Be A Fair Judge In Court? Estonia Thinks So"
1061:
975:. Stanford University, n.d. Web. 24 Apr. 2014. <
958:. Stanford University, n.d. Web. 23 Apr. 2014. <
704:, Vol. 23, No. 1 (Nov., 1970), pp. 40-62. At p. 40.
1233:Flatt, Amelie; Langner, Arne; Leps, Olof (2022).
1428:
1232:
1084:The White House. Office of the Press Secretary.
837:"The British Nationality Act as a logic program"
412:., and Surden's visualizations of Copyright Law.
247:There have also been many attempts to create a
939:International Conference on AI and Law (ICAIL)
262:-based standard proposed and developed by the
46:in human interaction and the proliferation of
1237:(1st ed.). Heidelberg: Sprinter Nature.
1008:. N.p., 19 Mar. 2013. Web. 17 Apr. 2014. <
775:
516:"Computational Law - The Cop in the Backseat"
73:(embodied in computer game playing systems).
1021:
1347:"Public Access to Court Electronic Records"
1181:Journal of International Dispute Settlement
736:
734:
732:
960:http://logic.stanford.edu/classes/cs204/
776:Prakken, H.; Sartor, G. (October 2015).
181:In a 1988 review of Anne Gardner's book
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622:
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342:Some research has also been focused on
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310:. Citation networks allow the use of
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990:. N.p., n.d. Web. 18 Apr. 2014. <
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417:Online legal resources and databases
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13:
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1258:Katz, Daniel Martin (4 May 2010).
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1109:
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242:
14:
1458:
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675:"Automation in the Legal World."
662:"Automation in the Legal World."
1306:Loyola of Los Angeles Law Review
753:90 Harv. L. Rev. 837-895 (1977).
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1135:389.19 (2010): 4201-208. Print.
1122:389.19 (2010): 4195-200. Print.
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461:Artificial intelligence and law
971:"Stanford Computational Law."
749:See, e.g., L. Thorne McCarty,
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582:
569:
560:
551:
522:
507:
492:
298:, it is possible to construct
1:
1447:Computational fields of study
577:Law and Contemporary Problems
486:
237:
1381:"The Supreme Court Database"
977:http://complaw.stanford.edu/
799:10.1016/j.artint.2015.06.005
501:"What is Computational Law?"
330:Another effort has examined
139:National Physical Laboratory
7:
1423:List of Legal Tech Startups
1209:12.5 (2006): 733-40. Print.
1106:15.3 (2006): 324-46. Print.
454:
306:of legal precedent, called
119:Modern Uses of Logic in Law
16:Branch of legal informatics
10:
1463:
1153:30.1 (2008): 16-30. Print.
973:Stanford Computational Law
76:
18:
1361:"Law Library of Congress"
1022:Freeman Engstrom, David.
845:Communications of the ACM
48:embedded computer systems
1088:. N.p., 9 May 2013. Web.
1006:Stephen Wolfram Blog RSS
956:CS204: Computers and Law
19:Not to be confused with
786:Artificial Intelligence
548:Vol 46 (2012): 629-676.
434:Law Library of Congress
332:United States Tax Court
268:University of Amsterdam
191:artificial intelligence
532:"Computable Contracts"
530:Surden, Harry (2012).
264:Leibniz Center for Law
251:or machine executable
101:aiming to incorporate
1193:10.1093/jnlids/idz007
599:12 March 2015 at the
588:"About Jurimetrics."
546:U.C. Davis Law Review
514:Genesereth, Michael.
499:Genesereth, Michael.
448:United States Reports
365:network visualization
344:hierarchical networks
1283:10.2139/ssrn.2097161
816:on 27 September 2020
766:3.2 (1990): 189-200.
481:Legal expert systems
359:empirical analysis.
38:sort already exist.
1437:Argument technology
1415:Stanford Law School
1366:Library of Congress
884:Stanford Law School
715:Stanford Law Review
702:Stanford Law Review
579:(1963): 5-35. At 8.
280:Ricardian Contracts
1104:Political Analysis
348:United States Code
286:Empirical analysis
1320:– via SSRN.
1293:– via SSRN.
1244:978-3-031-14131-7
858:10.1145/5689.5920
590:About the Journal
476:Legal informatics
427:Federal Judiciary
371:Examples of tools
308:citation networks
292:citation analysis
175:backward chaining
97:By 1949, a minor
56:self-driving cars
31:legal informatics
29:is the branch of
27:Computational Law
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1136:
1129:
1123:
1116:
1107:
1100:
1089:
1082:
1076:
1075:
1059:
1053:
1052:
1045:
1039:
1038:
1036:
1034:
1019:
1013:
1002:
996:
986:
980:
969:
963:
952:
946:
935:
929:
925:
919:
916:
910:
907:
901:
894:
888:
887:
876:
870:
869:
841:
832:
826:
825:
823:
821:
815:
809:. Archived from
782:
773:
767:
760:
754:
747:
741:
738:
727:
724:
718:
711:
705:
698:
692:
686:
680:
673:
667:
660:
654:
649:Niblett, Bryan.
647:
641:
635:
629:
624:
615:
610:
604:
586:
580:
573:
567:
564:
558:
555:
549:
543:
526:
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511:
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496:
400:Creative Commons
300:citation indices
249:machine readable
211:leaders remarked
1462:
1461:
1457:
1456:
1455:
1453:
1452:
1451:
1427:
1426:
1417:Legal Tech List
1411:CodeX Techindex
1407:
1402:
1395:"Bound Volumes"
1393:
1392:
1388:
1379:
1378:
1374:
1359:
1358:
1354:
1345:
1344:
1340:
1331:Surden, Harry.
1329:
1325:
1302:
1298:
1271:
1267:
1256:
1252:
1245:
1231:
1227:
1218:
1217:
1213:
1204:
1200:
1177:
1173:
1169:30 (2010): 523.
1164:
1157:
1151:Social Networks
1148:
1139:
1130:
1126:
1117:
1110:
1101:
1092:
1083:
1079:
1060:
1056:
1047:
1046:
1042:
1032:
1030:
1020:
1016:
1003:
999:
987:
983:
970:
966:
953:
949:
936:
932:
926:
922:
917:
913:
908:
904:
900:9.3 (1988): 45.
895:
891:
886:. 8 April 2021.
878:
877:
873:
839:
833:
829:
819:
817:
813:
780:
774:
770:
761:
757:
748:
744:
739:
730:
725:
721:
712:
708:
699:
695:
687:
683:
674:
670:
661:
657:
648:
644:
636:
632:
625:
618:
611:
607:
601:Wayback Machine
587:
583:
574:
570:
565:
561:
556:
552:
528:Surden, Harry.
527:
523:
512:
508:
497:
493:
489:
457:
419:
373:
356:
312:graph traversal
288:
245:
243:Algorithmic law
240:
225:has said that:
223:Stephen Wolfram
187:Edwina Rissland
79:
24:
17:
12:
11:
5:
1460:
1450:
1449:
1444:
1439:
1425:
1424:
1418:
1406:
1405:External links
1403:
1401:
1400:
1386:
1372:
1352:
1338:
1323:
1296:
1275:Cardozo L. Rev
1265:
1250:
1243:
1225:
1211:
1198:
1187:(2): 200–247.
1171:
1155:
1137:
1124:
1108:
1090:
1077:
1072:11245/1.425496
1054:
1040:
1014:
997:
981:
964:
947:
930:
920:
911:
902:
889:
871:
852:(5): 370–386.
827:
768:
755:
742:
728:
719:
706:
693:
681:
668:
655:
642:
630:
616:
605:
581:
568:
559:
550:
521:
506:
490:
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485:
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478:
473:
468:
463:
456:
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451:
444:
437:
430:
418:
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406:
403:
397:
372:
369:
355:
352:
296:legal citation
287:
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244:
241:
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231:
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218:
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99:academic field
78:
75:
15:
9:
6:
4:
3:
2:
1459:
1448:
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1254:
1246:
1240:
1236:
1229:
1221:
1215:
1208:
1202:
1194:
1190:
1186:
1182:
1175:
1168:
1162:
1160:
1152:
1146:
1144:
1142:
1134:
1128:
1121:
1115:
1113:
1105:
1099:
1097:
1095:
1087:
1081:
1073:
1069:
1065:
1058:
1050:
1044:
1029:
1025:
1018:
1011:
1007:
1001:
995:
993:
985:
978:
974:
968:
961:
957:
951:
944:
940:
934:
924:
915:
906:
899:
893:
885:
881:
875:
867:
863:
859:
855:
851:
847:
846:
838:
831:
812:
808:
804:
800:
796:
792:
788:
787:
779:
772:
765:
759:
752:
746:
737:
735:
733:
723:
716:
710:
703:
697:
690:
685:
678:
672:
665:
659:
652:
646:
639:
634:
628:
623:
621:
614:
609:
602:
598:
595:
591:
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578:
572:
563:
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547:
541:
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441:Supreme Court
438:
435:
431:
428:
424:
421:
420:
411:
410:stare decisis
407:
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401:
398:
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392:
388:
385:
381:
378:
375:
374:
368:
366:
360:
354:Visualization
351:
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338:
333:
328:
326:
321:
320:Supreme Court
316:
313:
309:
305:
301:
297:
293:
283:
281:
275:
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269:
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261:
256:
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235:
228:
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216:
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208:
204:
203:
202:
198:
194:
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188:
184:
179:
176:
170:
168:
162:
160:
154:
152:
151:Marvin Minsky
148:
144:
140:
135:
132:
130:
125:
121:
120:
114:
112:
108:
107:computational
104:
100:
95:
92:
88:
84:
74:
72:
68:
63:
61:
57:
53:
49:
45:
41:
35:
32:
28:
22:
1442:Computer law
1389:
1375:
1364:
1355:
1341:
1326:
1309:
1305:
1299:
1274:
1268:
1253:
1234:
1228:
1214:
1206:
1201:
1184:
1180:
1174:
1167:Va. Tax Rev.
1166:
1150:
1132:
1127:
1119:
1103:
1085:
1080:
1063:
1057:
1043:
1031:. Retrieved
1027:
1017:
1005:
1000:
989:
984:
972:
967:
955:
950:
938:
933:
923:
914:
905:
897:
892:
883:
874:
849:
843:
830:
820:27 September
818:. Retrieved
811:the original
790:
784:
771:
763:
758:
750:
745:
722:
714:
709:
701:
696:
688:
684:
676:
671:
663:
658:
650:
645:
637:
633:
626:
612:
608:
589:
584:
576:
571:
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545:
524:
509:
494:
361:
357:
341:
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317:
289:
276:
272:Barack Obama
257:
246:
232:
219:
215:
210:
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199:
195:
182:
180:
171:
163:
158:
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117:
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52:smart phones
36:
26:
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21:Computer law
1220:"Legal XML"
898:AI Magazine
793:: 214–245.
764:Ratio Juris
466:Jurimetrics
387:Akoma Ntoso
207:one article
129:Jurimetrics
111:jurimetrics
1431:Categories
1033:20 October
487:References
337:nonprofits
302:and large
253:legal code
238:Approaches
143:Teddington
124:statistics
103:electronic
91:automation
87:AI and law
380:Legal XML
325:precedent
147:Middlesex
67:contracts
50:(such as
1421:LawSites
928:215-319.
597:Archived
455:See also
44:Internet
40:TurboTax
1318:2040881
1291:2097161
866:5665107
807:4261497
540:2216866
394:Metalex
266:of the
167:Swansea
77:History
1316:
1312:: 77.
1289:
1241:
864:
805:
689:Ibid.
538:
471:Lawbot
389:, and
384:UNDESA
304:graphs
159:Taxman
60:robots
58:, and
1028:Wired
1012:>.
994:>.
962:>.
862:S2CID
840:(PDF)
814:(PDF)
803:S2CID
781:(PDF)
627:Ibid.
613:Ibid,
603:>.
423:PACER
377:OASIS
71:games
1314:SSRN
1287:SSRN
1239:ISBN
1035:2022
979:>
822:2023
691:768.
536:SSRN
446:The
439:The
432:The
105:and
1279:doi
1189:doi
1068:hdl
854:doi
795:doi
791:227
391:CEN
260:XML
141:in
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1433::
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1285:.
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850:29
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856::
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518:.
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131:.
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