Neural Networks in Legal Theory

Verenich, Vadim

doi:10.2478/sh-2024-0018

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.

References

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.
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
Hage, J. C. (2005). Studies in Legal Logic, Dordrecht: Springer.
Search in Google Scholar Back to article
Hart, H. L. A. (1958). Positivism and the Separation of Law and Morals, Harvard Law Review 71/4, pp. 593–629.
Search in Google Scholar Back to article
Hart, H. L. A. (1994). The Concept of Law, 2nd edition, Oxford: Oxford Press.
Search in Google Scholar Back to article
Hayek, F. A. (1976). Law Legislation and Liberty. Volume II. The Mirage of Social Justice, London: Routledge and Kegan Paul.
Search in Google Scholar Back to article
Hayek, F. A. (1977). New Studies in Philosophy, Politics, Economics and the History of Ideas, London: Routledge and Kegan Paul.
Search in Google Scholar Back to article
Haykin, S. (2006). Neural networks: a complete course, Moscow: William Publishing.
Search in Google Scholar Back to article
Henderson, W. D. (2013). A Blueprint for Change. The Pepperdine Law Review 40/2, Available at: https://digitalcommons.pepperdine.edu/plr/vol40/iss2/7.
Search in Google Scholar Back to article
Hobson, J. B., & Slee, D. (1994). Indexing the Theft Act 1968 for case based reasoning and artificial neural networks, In Proceedings of the Fourth National Conference on Law, Computers and Artificial Intelligence, unnumbered additions, Exeter: Exeter University Centre for Legal Interdisciplinary Development.
Search in Google Scholar Back to article
Hunter, D. (1994). Looking for law in all the wrong places: Legal theory and legal neural networks, In H. Prakken, A. J. Muntjewerff, A. Soeteman, and R. Winkels (eds.), Legal Knowledge Based Systems: The Relation with Legal Theory, Lelystad: Koninklijke Vermande, pp. 55–64.
Search in Google Scholar Back to article
Lakoff, G. (1987). Women, Fire And Dangerous Things: What Categories Tell Us About The Nature Of Thought, Chicago: University of Chicago Press.
Search in Google Scholar Back to article
Perelman, Ch. & Olbrechts-Tyteca, L. (1971). The New Rhetoric: A Treatise on Argumentation, Paris: Notre Dame Press.
Search in Google Scholar Back to article
Rosenblatt, F. (1962). Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, Washington: Spartan Books.
Search in Google Scholar Back to article
Seger, C. A. & Peterson, E. J. (2013). Categorisation=Decision Making + Generalisation, Neuroscience & Biobehavioural Reviews 37/7, pp. 1187–1200.
Search in Google Scholar Back to article
Stranieri, A., & Zeleznikow, J. (2005). Data Mining Using Neural Networks, In Knowledge Discovery from Legal Databases. Law and Philosophy Library, vol. 69, Dordrecht: Springer.
Search in Google Scholar Back to article
Stranieri, A., Zeleznikow, J., Gawler, M., & Bryn, L. (1999). A Hybrid Rule – Neural Approach For The Automation of Legal Reasoning in The Discretionary Domain of Family Law in Australia, Artificial Intelligence and Law 7/2–3, pp. 153–183.
Search in Google Scholar Back to article

Neural Networks in Legal Theory

Abstract

Paradigm

My account