<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

Neural Networks in Legal Theory

Studia Humana

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Neural Networks in Legal Theory"}

This article explores the domain of legal analysis and its methodologies, emphasising the significance of generalisation in legal systems. It discusses the...

Neural Networks in Legal Theory

Publié en ligne: 28 avr. 2024

Pages: 41 - 51

DOI: https://doi.org/10.2478/sh-2024-0018

Mots clés
legal analysis, generalisation, legal concepts, logical induction, semantic generalisation, formal-deductive approach, fuzzy logic, non-monotonic defeasible reasoning, artificial intelligence, machine learning, neural networks, deep learning algorithms, legal theory

© 2024 Vadim Verenich, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Neural Networks in Legal Theory

Publié en ligne: 28 avr. 2024

Pages: 41 - 51

DOI: https://doi.org/10.2478/sh-2024-0018

Mots cléslegal analysis, generalisation, legal concepts, logical induction, semantic generalisation, formal-deductive approach, fuzzy logic, non-monotonic defeasible reasoning, artificial intelligence, machine learning, neural networks, deep learning algorithms, legal theory

© 2024 Vadim Verenich, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
legal analysis, generalisation, legal concepts, logical induction, semantic generalisation, formal-deductive approach, fuzzy logic, non-monotonic defeasible reasoning, artificial intelligence, machine learning, neural networks, deep learning algorithms, legal theory