Legal expert system

legal expert system is a domain-specific expert system That uses artificial intelligence to emulate the decision-making abilities of a human expert in the field of law. [1] : 172 Legal expert systems employee a rule based or knowledge base and an inference engine to accumulate, reference and Produce expert knowledge is specific subjects dans le legal domain.


It has been suggested that it should be used for the purposes of this article. [2] Many of the first legal expert systems were created in the 1970s [1] : 179 and 1980s. [3] : 928

Lawyers were originally identified as primary target users of legal expert systems. [4] : 3 Potential motivations for this work included:

  • Speedier delivery of legal advice;
  • Labor intensive legal tasks;
  • Development of knowledge management;
  • Reduced overhead and labor costs and higher profitability for law firms; and
  • Reduced fees for clients. [5] : 439

Some early development work was directed towards the creation of automated judges. [6] : 386

Later work on legal expert systems has identified potential benefits to non-lawyers as a means of increasing access to legal knowledge. [4] : 4

Legal expert systems can also support administrative processes, facilitating decision making processes, automating rule-based analyzes [7] and exchanging information directly with citizen-users. [8]


Architectural variations

Rule-based expert systems rely on a model of deductive reasoning that uses “if A, then B” rules. In a rule-based legal expert system, information is represented in the form of deductive rules within the knowledge base. [9]

Case-based reasoning models, which store and manipulate examples or cases, hold the potential to emulate an analogical reasoning process to be well-suited for the legal domain. [9] This model effectively draws on experiences for outcomes for similar problems. [10] : 5

A neural net relies on a computer model that mimics that structure of a human brain, and operates in a very similar way to the case-based reasoning model. [9] This expert system model is capable of recognizing and classifying patterns within the realm of legal knowledge and dealing with imprecise inputs. [11] : 18

Fuzzy logic models to create ‘fuzzy’ concepts or objects that can be converted into quantitative terms or rules that are indexed and retrieved by the system. [11] : 18-19 In the legal domain, fuzzy logic can be used for rule-based and case-based reasoning models.

Theoretical variations

While some legal expert systems have adopted a very practical approach, employing scientific modes of reasoning within a given set of rules or cases, others have opted for a broader philosophical approach inspired by jurisprudential reasoning modes of emanating from established legal theoreticians. [1] : 183

Functional variations

Some legal expert systems, however, have a particular conclusion in law, while others are designed to predict a particular outcome. An example of a predictive system is one that predicts the outcome of judicial decisions, the value of a case, or the outcome of litigation. [3] : 932


Many forms of legal experts have become widely used and accepted by both the legal community and the users of legal services. [12] [13]


Domain-related problems

The inherent complexity of law as a discipline raises immediate challenges for legal expert knowledge knowledge engineers . Legal Matters and Interrelated Facts and Issues, which further compound the complexity. [5] : 4 [6] : 386

Factual uncertainty may also arise when there are disputed versions of factual representations that must be input into an expert system to begin the reasoning process. [5] : 4

Computerized problem solving

The limitations of most computerized problem solving techniques inhibits the success of many expert systems in the legal domain. Expert systems typically rely on deductive reasoning models that have difficulty depending on the nature of the decision. [9]

Representation of legal knowledge

Expert legal knowledge can be difficult to represent or formalize within the structure of an expert system. For knowledge engineers, challenges include:

  • Open texture : Law is rarely applied in an exact way to specific facts, and exact outcomes are rarely a certainty. Statutes may be interpreted according to different linguistic interpretations, reliance on precedent or other contextual factors including a particular judge’s conception of fairness. [5] : 4
  • The balancing of reasons: Many arguments involve considerations or reasons that are not easily represented in a logical way. For instance, many constitutional legal issues are independently well-established considerations for state interests against individual rights. [14] Such balancing may draw on extra-legal considerations that would be difficult to represent logically in an expert system.
  • Indeterminacy of legal reasoning: In the adversarial arena of law, it is common to have two strong arguments on a single point. Determining the ‘right’ answer. [6] : 386-387

Time and cost effectiveness

Creating a functioning expert system requires significant investments in software architecture , subject matter expertise and knowledge engineering . Faced with these challenges, many system architects restrict the domain in terms of subject matter and jurisdiction. The consequence of this approach is the creation of narrowly focused and geographically restricted legal systems. [5] : 5

Lack of correctness in results or decisions

Legal expert systems may lead non-expert users to incorrect or inaccurate results and decisions. This problem could be compounded by the fact that users may rely heavily on the correctness or trustworthiness of results or decisions generated by these systems. [15]


ASHSD-II is a hybrid legal expert system that blends rule-based and case-based reasoning models in the area of ​​matrimonial property disputes under English law. [10] : 49

CHIRON is a hybrid legal expert system that blends rule-based and case-based models. [16]

JUDGE is a rule-based legal system that deals with sentencing in the criminal law domain for murder, assault and manslaughter. [17] : 51

The Latent Damage Project is a rule-based legal expert That system deals with limitation periods under the (UK) Latent Damage Act 1986 in relation to the domains of tort, contract and product liability law. [18]

Split-Up is a rule-based legal expert That system assists in the division of marital assets selon the (Australia) Family Law Act (1975) . [19]

SHYSTER is a case-based legal system that can also function as a hybrid through its ability to link with rule-based models. It was designed to accommodate multiple legal domains, including aspects of Australian copyright law, contract law, personal property and administrative law. [17]

TAXMAN is a rule-based system that could perform a basic form of legal reasoning by classifying boxes under a particular category of statutory rules in the area of ​​law concerning corporate reorganization. [20] : 837


There May be a Lack of consensus over what distinguishes a legal expert system from a knowledge-based system (also called Expired an intelligent knowledge-based system). A legal expert is a human rights expert who is a human rights expert. True legal expert systems typically focus on a narrow domain of expertise as opposed to a specific knowledge-based systems. [5] : 1

Legal expert systems represent potentially disruptive technologies for the traditional, bespoke delivery of legal services. Accordingly, established legal practitioners may consider themselves a threat to historical business practices. [5] : 2

Arguments have been made to take into consideration various theoretical approaches to legal decision making. [1] : 190 Meanwhile, some legal expert system architects contend that for many lawyers have proficient legal reasoning skills without a sound base in legal theory, [17] : pp.6-7

Because legal expert systems apply precision and scientific rigor to the act of legal decision-making, they may be seen as a challenge to the more disorganized and less precise judgments of traditional jurisprudential modes of legal reasoning. [20] : 839 Some commentators also contend that the true nature of legal practice does not necessarily depend on analyzes of legal rules or principles; Decisions are based on the expectation of what a human adjudicator would decide for a given case. [3] : 930

Recent developments

Since 2013, there have been significant developments in legal expert systems. Professor Tanina Rosent of Georgetown Law Center teaches a course in designing legal expert systems. [21] Companies such as Neota Logic have begun to offer artificial intelligence and machine learning -based legal expert systems. [22]

See also

  • Applications of artificial intelligence
  • Artificial intelligence and law
  • Clinical decision support system
  • Disruptive innovation
  • HYPO CBR , a legal expert system
  • Indeterminacy debate in legal theory
  • Subject-matter expert


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  6. ^ Jump up to:a b c Schafer, Burkhard (2010). “ZombAIs: Legal Expert Systems as Representatives” Beyond the Grave ” ” . SCRIPTed . 7 (2) . Retrieved 26 October 2012 .
  7. Jump up^ Lodder, Arno; Zeleznikow, John (2005). “Developing an Online Dispute Resolution Environment: Dialogue Tools and Negotiation Support Systems in a Three-Step Model”. Harvard Negotiation Law Review . 10 : 293. SSRN  1008802  .
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  11. ^ Jump up to:a b Main, Julie; Pal, Sankar K .; Dillon, Tharam; Shiu, Simon (2001). “A Tutorial on Case-Based Reasoning”. In Soft Computing in Case Based Reasoning (PDF) (4th ed.). London: (Ltd) . Retrieved 26 October 2012 .
  12. Jump up^ Ambrogi, Robert. “Latest legal victory has LegalZoom poised for growth.” ABA Journal. American Bar Association, Aug 1 2014. Web. 17 June 2017. <>.
  13. Jump up^ Np, nd Web. 16 June 2017. <>.
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  16. Jump up^ Sanders, Kathryn E. (1991). “Representing and reasoning about open-textured predicates”. ICAIL ’91: Proceedings of the 3rd International Conference on Artificial Intelligence and Law . ICAIL. pp. 140-141.
  17. ^ Jump up to:a b c Popple, James (1996). A Pragmatic Legal Expert System (PDF) . Applied Legal Philosophy Series. Dartmouth (Ashgate). ISBN  1-85521-739-2 . Archived from the original on 28 December 2006 . Retrieved 10 August 2014 . Also available at Google Books .
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  19. Jump up^ Zeleznikow, John; Stranieri, Andrew; Gawler, Mark (1996). “Project Report: Split-Up – A Legal Expert System which Determines Property Division upon Divorce”. Artificial Intelligence and Law . 3 : 268.
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  22. Jump up^, “Automating Legal Advice: AI and Expert Systems,” Bloomberg Law Big Law Business, January 22 , 2016.

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