Agents on the Bench: Large Language Model Based Multi Agent Framework for Trustworthy Digital Justice
This work addresses the need for trustworthy AI in the justice system to enhance public trust, though it appears incremental as it builds on existing LLM methods for a specific domain.
The paper tackles the problem of improving transparency and explainability in AI for judicial decision-making by proposing AgentsBench, a multi-agent framework based on large language models that simulates judicial deliberation, and it outperforms existing LLM-based methods in legal judgment prediction tasks.
The justice system has increasingly employed AI techniques to enhance efficiency, yet limitations remain in improving the quality of decision-making, particularly regarding transparency and explainability needed to uphold public trust in legal AI. To address these challenges, we propose a large language model based multi-agent framework named AgentsBench, which aims to simultaneously improve both efficiency and quality in judicial decision-making. Our approach leverages multiple LLM-driven agents that simulate the collaborative deliberation and decision making process of a judicial bench. We conducted experiments on legal judgment prediction task, and the results show that our framework outperforms existing LLM based methods in terms of performance and decision quality. By incorporating these elements, our framework reflects real-world judicial processes more closely, enhancing accuracy, fairness, and society consideration. AgentsBench provides a more nuanced and realistic methods of trustworthy AI decision-making, with strong potential for application across various case types and legal scenarios.