Affiliation:
1. Independent researcher Tartu , Estonia
Abstract
Abstract
This article explores the domain of legal analysis and its methodologies, emphasising the significance of generalisation in legal systems. It discusses the process of generalisation in relation to legal concepts and the development of ideal concepts that form the foundation of law. The article examines the role of logical induction and its similarities with semantic generalisation, highlighting their importance in legal decision-making. It also critiques the formal-deductive approach in legal practice and advocates for more adaptable models, incorporating fuzzy logic, non-monotonic defeasible reasoning, and artificial intelligence. The potential application of neural networks, specifically deep learning algorithms, in legal theory is also discussed. The article discusses how neural networks encode legal knowledge in their synaptic connections, while the syllogistic model condenses legal information into axioms. The article also highlights how neural networks assimilate novel experiences and exhibit evolutionary progression, unlike the deductive model of law. Additionally, the article examines the historical and theoretical foundations of jurisprudence that align with the basic principles of neural networks. It delves into the statistical analysis of legal phenomena and theories that view legal development as an evolutionary process. The article then explores Friedrich Hayek’s theory of law as an autonomous self-organising system and its compatibility with neural network models. It concludes by discussing the implications of Hayek’s theory on the role of a lawyer and the precision of neural networks.
Reference18 articles.
1. Borges, F., Borges, R., & Bourcier, D. (2003). Artificial neural networks and legal categorisation, In The 16th Annual Conference on Legal Knowledge and Information Systems (JURIX’03 ), The Netherlands, 11–12 December 2003, pp. 11–21.
2. Camuñas-Mesa, L. A., Linares-Barranco, B., & Serrano-Go Gotarredona, T. (2019). Neuromorphic Spiking Neural Networks and Their Memristor-CMOS, Hardware Implementations. Materials 12, p. 2745.
3. EE 260 (Spring 2020). Advanced VLSI Design for Machine Learning and AI. Available at: https://vsclab.ece.ucr.edu/courses/2019/12/01/ee-260-spring-2020-advanced-vlsi-design-machine-learning-and-ai.
4. Hage, J. C. (2005). Studies in Legal Logic, Dordrecht: Springer.
5. Hart, H. L. A. (1958). Positivism and the Separation of Law and Morals, Harvard Law Review 71/4, pp. 593–629.