Top-Down and Bottom-Up Legal System as a Gödel Machine With Object-Oriented Framework

Abstract

The past decade has witnessed the exponential growth of data-driven industrial revolutions, from e-commerce to the internet of things (IoT), and from social media to its notorious abuses. A similar albeit milder trend is swiping across the landscape of the legal profession has experienced the radical changes brought by advancements in Artificial Intelligence (“AI”) in recent years. There were two dominant approaches to digitally represent the legal reasoning: the top-down approach where legal experts were engaged to analyze and design the legal system by its operational parts, while in contrast, the bottom-up approach uses machine learning to build up models for legal reasoning from the decided cases. the top-down approach focuses on representing the domain knowledge into Ontological Structures that can be better communicated to the computer scientist in a CRISP-DM cycle. Top-down approach has long been employed in the legal profession to communicate legal concepts and logic relationships concisely and precisely. Vigorous attempts have been made to translate natural-language-based legal texts into machine-readable (executable) versions through tools such as Ontology Web Language (OWL). The top-down approach, despite its conceptual simplicity and clarity, suffers from a significate drawback: knowledge acquisition bottleneck, that that it is expensive to create, maintain and update such system. On the other hand, the bottom-up approach has been taking the headlines in recent years with vendors of legal analytic products that allegedly beaten the best lawyers in legal analysis for the specialized domain of the law while charging a fraction of the fee. However, one must stop to ponder upon what stands behind the miraculous veil of the technological black box: the irresistible temptation to improve the predictive accuracy, and when the algorithm runs out of permissible, admissible, and legal material of a case, it embarks on extraneous factors: parties, their lawyers, judges, and even judge’s breakfasts, which are the “prohibited zone of reasoning” that the law consistently seeks to outlaw. In light of the ambitious progress to digitize the law, some normative and doctrinal controls must be in place to ensure that the learning programs do not merely learn from whatever the past offers but learn the desirable normative values as we aspire to enshrine in our legal systems. This paper, therefore, seeks to establish the fabrics of a framework that represents legal system using autopoiesis system conception top-down while building up the ontological infrastructure with Object-Method-Property model bottom-up, which may together serve as a useful bridge between the two dominate approaches while maintaining the legal system as we know it. This paper will start top-down by discussing the system dynamics of the law as a self-reproducing self-referential closed autopoiesis social system and then advances to analyze Godel Machine concept (“GM”) in computer science may be employed to digitally re-produce the organics of the law as a social system. Then, this paper will present some fundamentals of the Object-oriented programming (“OOP”) paradigm and how its inherent similarity to the legal reasoning forms the foundation to fit the building stocks from existing legal materials bottom-up into the scaffold of the system of law designed top-down. A line must be drawn between the part of the law that should be built top-down and the part bottom-up. The importance of maintaining a human-centred top layer of the law and the benefit of automating the lower layers through machine learning must be balanced. The OOP-GM model could have multiple benefits. First, it can maintain the adaptivity of the legal system to social changes while consistently enforcing normative expectations. Second, the OOP-GM offers a comprehensive model of ontological construction of legal concepts while maintaining compatibility to integrate with most objected-reoriented programming languages. Thirdly, OOP-GM offers greater analytical precision for the communication of law which provides for greater general access to the law as well as structural clarity for legal drafting, practices, and education. Lastly, machine learning functions with specific control on the input parameters is very unlikely to be influenced by extraneous factors, which provides for greater fairness and confidence in the legal system.