Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Reason and Intuition

Reason and Intuition

The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the brain can only use...

Non-equilibrium in the Cell

Non-equilibrium in the Cell

An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...

Sources of Law

Sources of Law

Laws form the essential rules set by governing authorities to shape and control societal behavior. In nursing, laws guide actions, safeguard patient rights, define nurses' scope of practice, and maintain professional standards. Understanding the legal framework governing nursing involves recognizing four primary sources of law: constitutional, statutory, administrative (regulatory), and common law.
Constitutional law is foundational, deriving from federal and state constitutions, and...

Introduction to Cognitive Psychology

Introduction to Cognitive Psychology

Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
This field emerged in the mid-20th century, following a period dominated by behaviorism, which...

Language and Cognition

Language and Cognition

Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.

Automatic Processing and Automatic Social Behavior

Automatic Processing and Automatic Social Behavior

Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

UP-Fall Detection Dataset: A Multimodal Approach.

Sensors (Basel, Switzerland)·2019

Same author

An Intelligent Failure Detection on a Wireless Sensor Network for Indoor Climate Conditions.

Sensors (Basel, Switzerland)·2019

Same author

A Novel Artificial Organic Control System for Mobile Robot Navigation in Assisted Living Using Vision- and Neural-Based Strategies.

Computational intelligence and neuroscience·2019

See all related articles

Search research articles

Related Experiment Video

Updated: May 11, 2026

Visualizing Lignification Dynamics in Plants with Click Chemistry: Dual Labeling is BLISS!

Visualizing Lignification Dynamics in Plants with Click Chemistry: Dual Labeling is BLISS!

Published on: January 26, 2018

Causal Artificial Intelligence in Legal Language Processing: A Systematic Review.

Philippe Prince Tritto¹, Hiram Ponce²

¹Facultad de Derecho, Universidad Panamericana, Augusto Rodin 498, Mexico City 03920, Mexico.

Entropy (Basel, Switzerland)

|April 26, 2025

Summary

This summary is machine-generated.

Causal Artificial Intelligence (AI) shows promise for legal reasoning over correlation-based AI. However, challenges like legal uncertainty, scalability, and bias must be addressed for effective implementation in legal language processing.

Keywords:

causal artificial intelligence causal inference causal machine learning legal AI legal language processing legal reasoning legal text analysis machine learning natural language processing systematic review

More Related Videos

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Related Experiment Videos

Last Updated: May 11, 2026

Visualizing Lignification Dynamics in Plants with Click Chemistry: Dual Labeling is BLISS!

Visualizing Lignification Dynamics in Plants with Click Chemistry: Dual Labeling is BLISS!

Published on: January 26, 2018

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Artificial Intelligence
Legal Technology
Computational Linguistics

Background:

Traditional correlation-based AI methods in legal language processing exhibit limitations.
Causal Artificial Intelligence (AI) offers a potential alternative for enhancing legal reasoning.
The integration of AI in law necessitates exploring advanced causal inference techniques.

Purpose of the Study:

To systematically review and analyze the challenges, limitations, and impact of Causal AI in legal language processing.
To compare the efficacy of Causal AI against correlation-based AI methods in legal contexts.
To identify research gaps and future directions for Causal AI in legal applications.

Main Methods:

Systematic literature review following Joanna Briggs Institute methodology.
Analysis of 47 papers (2017-2024) from academic, private sector, and policy sources.
Rigorous scoring framework evaluating Causal AI implementation, legal relevance, interpretability, and methodological quality.

Main Results:

Causal AI frameworks demonstrate superior capability in capturing legal reasoning compared to correlation-based methods.
Significant challenges include handling legal uncertainty, computational scalability, and algorithmic bias.
Current research shows a scarcity of real-world implementations and an overreliance on non-causal transformer architectures.

Conclusions:

Causal AI holds significant potential for advancing legal reasoning and language processing.
Addressing challenges in uncertainty, scalability, and bias is crucial for practical adoption.
Future research should focus on integrating AI innovation with law's narrative functions, emphasizing scalable and interpretable causal architectures.