  This study consists of a novel approach toward the analysis of court
judgments spanning five countries, including the United States, the United
Kingdom, Rwanda, Sweden and Hong Kong. This study also explores the
intersection of the latest advancements in artificial intelligence (AI) and
legal analysis, emphasizing the role of AI (specifically generative AI) in
identifying human biases and facilitating automated, valid, and coherent
multisided argumentation of court judgments with the goal of ensuring
consistent application of laws in and across various jurisdictions. By
incorporating Advanced Language Models (ALMs) and a newly introduced human-AI
collaborative framework, this paper seeks to analyze Grounded Theory-based
research design with Advanced Language Models (ALMs) in the practice of law.
SHIRLEY is the name of the AI-based application (built on top of OpenAI's GPT
technology), focusing on detecting logical inconsistencies and biases across
various legal decisions. SHIRLEY analysis is aggregated and is accompanied by a
comparison-oriented AI-based application called SAM (also an ALM) to identify
relative deviations in SHIRLEY bias detections. Further, a CRITIC is generated
within semi-autonomous arbitration process via the ALM, SARA. A novel approach
is introduced in the utilization of an AI arbitrator to critically evaluate
biases and qualitative-in-nature nuances identified by the aforementioned AI
applications (SAM in concert with SHIRLEY), based on the Hague Rules on
Business and Human Rights Arbitration. This Semi-Automated Arbitration Process
(SAAP) aims to uphold the integrity and fairness of legal judgments by ensuring
a nuanced debate-resultant "understanding" through a hybrid system of AI and
human-based collaborative analysis.
