Neural Networks in Legal Theory

• Autorzy: Vadim Verenich
• Języki publikacji: EN

Abstrakty

EN

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 formaldeductive 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. (original abstract)

Słowa kluczowe

EN

Law theory; Logical analysis; Fuzzy logic; Logic of induction; Artificial intelligence; Artificial neural networks (ANN); Algorithms; Learning of neural networks; Machine learning

PL

Teoria prawa; Analiza logiczna; Logika rozmyta; Logika indukcji; Sztuczna inteligencja; Sztuczne sieci neuronowe (SSN); Algorytmy; Uczenie sieci neuronowych; Uczenie maszynowe

• Czasopismo: Studia Humana
• Rocznik: 2024
• Tom: 13(3)
• Strony: 41--51

Twórcy

autor

Vadim Verenich

Bibliografia

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Identyfikatory

DOI

10.2478/sh-2024-0018