Legal expert systems have been used since the 1980's. Most of them are based on proprietary internal logic programmed exclusively for their own usage, although some of them involve open source legal domain specific programming languages intended for widespread use in computerised expression of human laws.
Expert systems are being used in a wide range of jurisdictions and areas of law, helping a diverse spectrum of users from members of the public to judges in making relevant decisions. However, there are so many laws that no current system covers them all. For illustration, the World Legal Information Institute project currently lists 1829 databases from 123 jurisdictions and the European Union alone enacted over 100,000 legislative acts since 1957. Not surprisingly the numbers have been ever growing: the EU has enacted 1,465 new legal acts since the beginning of 2017. Similarly, the 93rd Congress enacted the total of 26,222 legislative acts and the current 115th has enacted 7,353 acts only since January 2017. The growth of case law does not lag behind either.
The Features of an Expert System
An expert system should provide instant answers to legal situations in any jurisdiction or a combination of jurisdictions, lead through procedures and generate output such as legal documents or execute tasks, e. g. alert the authorities or instruct physical control mechanisms, while eliminating repetitive and time consuming human tasks. There is a huge potential to create numerous new jobs for legal technologists if its internal logic is easy to program.
It can work in a legal capacity or as a compliance application in both public and private sectors. In assessing a given situation it should consider all issues and possibilities, find pathways, and test multiple scenarios. Its inherent feature as a computer technology is that it does not forget or omit. It also does not fail to detect breaches and so it can unmistakably take relevant action and it can even enable preventive detection of loopholes in law.
It should enable constant improvements either through human input or self learning capability. Further it should provide high confidentiality and portability either as a self contained system running on a standalone device or a widely accessible service as a server based or distributed network application. It should be easy to integrate with input and output interfaces such as document content retrieval systems, speech recognition and synthesis, surveillance technologies and with artificial intelligence applications.
Its high market viability would be ideally based on the most widely supported open source technologies; in comparison with humans it is highly reliable and efficient as it works at the speed of its hardware and the cost of electricity.
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Note: For fully referenced text see the white paper at http://www.LegalMachinery.com/