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Catalyst for Common Law Evolution: Experiment with ChatGPT and a Hypothetical Common Law Jurisdiction

Abstract
This paper aims to carry out empirical analysis of the viability of large language models (LLMs) specifically ChatGPT in simulating the common law system and facilitating its evolutionary processes. Drawing on the Theory of Rules Evolution it is understood that common law generates efficient rules by natural selection through constant litigation. Nonetheless this evolutionary mechanism faces several hindrances. The process of change is typically slow and incremental. Courts often have to wait for a case that’s deemed ‘appropriate’ before they can change the law leading to extended delays. Additionally courts frequently struggle to make efficient decisions due to limited information. Other factors that decelerate the creation of efficient rules include judicial bias unequal distribution of resources among litigating parties and the diminishing presence of a competitive legal order. This study first assesses ChatGPT’s capability to embrace the essence of the common law system namely the doctrine of stare decisis. We then assess its potential to overcome the hindrances in common law development and promote efficient rules. Through a series of meticulously designed hypothetical cases set in a virtual jurisdiction called the “Matrix Kingdom” we observed that ChatGPT mimic the functions of a common law court by citing following and distinguishing its own precedents but it accomplishes this with significantly fewer resources and in less time. This implies that humans can introduce h
Authors
Iu Kwan Yuen and Zhou Ziyue Iu Kwan Yuen: c/o Pacific Chambers HKSAR China Zhou Ziyue: The University of Hong Kong Faculty of Law HKSAR China
Keywords
artificial intelligence ; ChatGPT ; common law system ; efficient rules ; judicial decision-making (search for similar items in EconPapers)
Rank
0.64
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Artificial Intelligence
Series
Asian Journal of Law and Economics 2024 vol. 15 issue 1 55-82
Time Added
2024/03/18 03:33
Total Downloads
0
Year Published
2024
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