[4] Artificial intelligence is also frequently employed in modeling the legal ontology, "an explicit, formal, and general specification of a conceptualization of properties of and relations between objects in a given domain".
It is also concerned to contribute in the other direction: to export tools and techniques developed in the context of legal problems to AI in general.
Embedded in the environment of the semantic web, it forms the basis for a heterogenous yet interoperable ecosystem, with which these tools can operate and communicate, as well as for future applications and use cases based on digital law or rule representation.
[27] In 2019, the city of Hangzhou, China established a pilot program artificial intelligence-based Internet Court to adjudicate disputes related to ecommerce and internet-related intellectual property claims.
[28]: 124 Parties appear before the court via videoconference and AI evaluates the evidence presented and applies relevant legal standards.
A variety of formalisms have been used, including propositional and predicate calculi; deontic, temporal and non-monotonic logics; and state transition diagrams.
Prakken and Sartor[31] give a detailed and authoritative review of the use of logic and argumentation in AI and Law, together with a comprehensive set of references.
[10] In the late 1970s and throughout the 1980s a significant strand of work on AI and Law involved the production of executable models of legislation, originating with Thorne McCarty's TAXMAN[13] and Ronald Stamper's LEGOL.
[14] TAXMAN was used to model the majority and minority arguments in a US Tax law case (Eisner v Macomber), and was implemented in the micro-PLANNER programming language.
However, the formalisation of a large portion of the British Nationality Act by Sergot et al.[18] showed that the natural language of legal documents bears a close resemblance to the Horn clause subset of first order predicate calculus.
Later work on larger applications, such as that on Supplementary Benefits,[32] showed that logic programs need further extensions, to deal with such complications as multiple cross references, counterfactuals, deeming provisions, amendments, and highly technical concepts (such as contribution conditions).
One of the earliest examples of a working quantitative legal prediction model occurred in the form of the Supreme Court forecasting project.
[55] Some academics and legal technology startups are attempting to create algorithmic models to predict case outcomes.
[59] Within the practice issues conceptual area, progress continues to be made on both litigation and transaction focused technologies.
Though predictive coding has largely been applied in the litigation space, it is beginning to make inroads in transaction practice, where it is being used to improve document review in mergers and acquisitions.
Legal services have traditionally been a "bespoke" product created by a professional attorney on an individual basis for each client.
[64] However, to work more efficiently, parts of these services will move sequentially from (1) bespoke to (2) standardized, (3) systematized, (4) packaged, and (5) commoditized.