This study explores the integration of Advanced Language Models (ALMs) and a collaborative framework between humans and AI to analyze court judgments. The use of SHIRLEY and SAM applications highlights the detection of biases and logical inconsistencies in legal decisions. The introduction of SARA in a Semi-Automated Arbitration Process (SAAP) aims to maintain fairness in legal judgments through a hybrid system of human-AI collaboration.
The research methodology combines constructivist Grounded Theory with qualitative analysis traditions, leveraging ALMs to categorize content accurately. By analyzing various court judgments from different countries, the study reveals patterns related to market activities, consumer rights protections, tax avoidance schemes, and legal accountability. The dialogue between SHIRLEY, SAM, and SARA showcases an innovative approach to evaluating biases in legal literature.
The study emphasizes the need for explainable AI capabilities within legal systems to enhance transparency and accountability. Future research could focus on larger datasets for analysis while ensuring prompt engineering strategies are optimized for repeatability within the legal domain. The findings suggest that integrating additional SAAP mechanisms can prevent misuse of AI in undermining judicial decisions.
To Another Language
from source content
arxiv.org
Önemli Bilgiler Şuradan Elde Edildi
by Michael De'S... : arxiv.org 03-01-2024
https://arxiv.org/pdf/2402.04140.pdfDaha Derin Sorular