Applied Artificial Intelligence for Law: The Use of Expert Systems and Decision Trees in Legal Practice and the Potential of Artificial Neural Networks

Authors: AlexMatheson, Published date: 18-04-2024 Status: Published

The literature on Artificial Intelligence (AI) as applied to legal practice can contain apparent paradoxes. On one hand, some researchers suggest that AI can trans- form law. On the other hand, others suggest that AI has more limited potential. This apparent tension can be under- stood by distinguishing different forms of AI technology. This paper reviews two contrasting types: It first examines expert systems and their component ‘decision trees’ and then turns to consider Artificial Neural Networks (ANNs). It reviews the use of the former within the legal sector and the potential of the latter. Expert systems have fixed ‘knowledge’ that is codified into computer systems by hu- man experts and, to some extent, enjoy wide legal-sector adoption–at least for the ‘decision tree’ part of the technol- ogy. Improvements for the legal sector can be made by en- riching decision trees using methods from decision science and by using algorithmically-generated decision trees. ANNs are quite different. These are vast networks of con- nections within software systems that ‘learn’ from rich data by being exposed to examples. This technology simulates the way that connections between neurons in a biological brain are strengthened via training. This paper finds that ANNs have significant potential but face more limited le- gal-sector adoption than decision trees. Other forms of AI exist but are beyond the scope of this paper.

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Corresponding author. Alex Matheson


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