Legal expert system
A 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.:172 Legal expert systems employ a rule base or knowledge base and an inference engine to accumulate, reference and produce expert knowledge on specific subjects within the legal domain.
- 1 Purpose
- 2 Types
- 3 Reception
- 4 Challenges
- 5 Examples
- 6 Controversies
- 7 Recent developments
- 8 See also
- 9 References
- 10 External links
It has been suggested that legal expert systems could help to manage the rapid expansion of legal information and decisions that began to intensify in the late 1960s. Many of the first legal expert systems were created in the 1970s:179 and 1980s.:928
Lawyers were originally identified as primary target users of legal expert systems.:3 Potential motivations for this work included:
- speedier delivery of legal advice;
- reduced time spent in repetitive, labour intensive legal tasks;
- development of knowledge management techniques that were not dependent on staff;
- reduced overhead and labour costs and higher profitability for law firms; and
- reduced fees for clients.:439
Some early development work was oriented toward the creation of automated judges.:386
Later work on legal expert systems has identified potential benefits to non-lawyers as a means to increase access to legal knowledge.:4
Rule-based expert systems rely on a model of deductive reasoning that utilizes "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.
Case-based reasoning models, which store and manipulate examples or cases, hold the potential to emulate an analogical reasoning process thought to be well-suited for the legal domain. This model effectively draws on known experiences our outcomes for similar problems.: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. This expert system model is capable of recognizing and classifying patterns within the realm of legal knowledge and dealing with imprecise inputs.:18
Fuzzy logic models attempt to create 'fuzzy' concepts or objects that can then be converted into quantitative terms or rules that are indexed and retrieved by the system.:18–19 In the legal domain, fuzzy logic can be used for rule-based and case-based reasoning models.
While some legal expert system architects 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 emanating from established legal theoreticians. :183
Some legal expert systems aim to arrive at 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.:932
The inherent complexity of law as a discipline raises immediate challenges for legal expert system knowledge engineers. Legal matters often involve interrelated facts and issues, which further compound the complexity.:4:386
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.:4
Computerized problem solving
The limitations of most computerized problem solving techniques inhibit the success of many expert systems in the legal domain. Expert systems typically rely on deductive reasoning models that have difficulty according degrees of weight to certain principles of law or importance to previously decided cases that may or may not influence a decision in an immediate case or context.
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 cases or other contextual factors including a particular judge's conception of fairness.: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 said to balance independently well-established considerations for state interests against individual rights. 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 may depend on a majority vote among expert judges, as in the case of an appeal.: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 expert systems that are difficult to justify on a cost-benefit basis.: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.
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.:49
CHIRON is a hybrid legal expert system that blends rule-based and case-based reasoning models to support tax planning activities under United States tax law and codes.
JUDGE is a rule-based legal expert system that deals with sentencing in the criminal legal domain for offences relating to murder, assault and manslaughter.:51
The Latent Damage Project is a rule-based legal expert system that deals with limitation periods under the (UK) Latent Damage Act 1986 in relation to the domains of tort, contract and product liability law.
SHYSTER is a case-based legal expert 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.
TAXMAN is a rule-based system that could perform a basic form of legal reasoning by classifying cases under a particular category of statutory rules in the area of law concerning corporate reorganization.:837
There may be a lack of consensus over what distinguishes a legal expert system from a knowledge-based system (also called an intelligent knowledge-based system). While legal expert systems are held to function at the level of a human legal expert, knowledge-based systems may depend on the ongoing assistance of a human expert. True legal expert systems typically focus on a narrow domain of expertise as opposed to a wider and less specific domain as in the case of most knowledge-based systems.:1
Legal expert systems represent potentially disruptive technologies for the traditional, bespoke delivery of legal services. Accordingly, established legal practitioners may consider them a threat to historical business practices.:2
Arguments have been made that a failure to take into consideration various theoretical approaches to legal decision making will produce expert systems that fail to reflect the true nature of decision making.:190 Meanwhile, some legal expert system architects contend that because many lawyers have proficient legal reasoning skills without a sound base in legal theory, the same should hold true for legal expert systems.: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 dynamics of traditional jurisprudential modes of legal reasoning.:839 Some commentators also contend that the true nature of legal practice does not necessarily depend on analyses of legal rules or principles; decisions are based instead on an expectation of what a human adjudicator would decide for a given case.:930
Since 2013, there have been significant developments in legal expert systems. Professor Tanina Rostain of Georgetown Law Center teaches a course in designing legal expert systems. Companies such as Neota Logic have begun to offer artificial intelligence and machine learning-based legal expert systems.
- Applications of artificial intelligence
- Artificial intelligence and law
- Computer-assisted legal research
- Clinical decision support system
- HYPO CBR, a legal expert system
- Indeterminacy debate in legal theory
- Subject-matter expert
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- Ambrogi, Robert. "Latest legal victory has LegalZoom poised for growth." ABA Journal. American Bar Association, 1 Aug. 2014. Web. 17 June 2017. <http://www.abajournal.com/magazine/article/latest_legal_victory_has_legalzoom_poised_for_growth>.
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- 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.
- Susskind, Richard (1989). "The latent damage system: a jurisprudential analysis". ICAIL '89: Proceedings of the 2nd international conference on artificial intelligence and law. ICAIL. pp. 23–32.
- 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.
- McCarty, L. Thorne (1997). "Reflections on Taxman: An Experiment in Artificial Intelligence and Legal Reasoning". Harvard Law Review. 90 (5).
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