# Advancing Legal Reasoning: The Integration of AI to Navigate Complexities and Biases in Global Jurisprudence with Semi-Automated Arbitration Processes (SAAPs)

arXiv: https://arxiv.org/abs/2402.04140 · HTML: https://mlsanity.com/paper/2402.04140/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 2 of its 12 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)

## Builds on (most cited first, 2 of 2)

- [Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models](https://mlsanity.com/paper/2304.03738/) (2023)
- [Black-box Prompt Learning for Pre-trained Language Models](https://mlsanity.com/paper/2201.08531/) (2022)

## Built on by (most cited first, 1 of 1)

- [Knowledge Acquisition on Mass-shooting Events via LLMs for AI-Driven Justice](https://mlsanity.com/paper/2504.12545/) (2025)

## Abstract (arXiv)

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.