CLAIM: An Intent-Driven Multi-Agent Framework for Analyzing Manipulation in Courtroom Dialogues (2506.04131v1)

Abstract: Courtrooms are places where lives are determined and fates are sealed, yet they are not impervious to manipulation. Strategic use of manipulation in legal jargon can sway the opinions of judges and affect the decisions. Despite the growing advancements in NLP, its application in detecting and analyzing manipulation within the legal domain remains largely unexplored. Our work addresses this gap by introducing LegalCon, a dataset of 1,063 annotated courtroom conversations labeled for manipulation detection, identification of primary manipulators, and classification of manipulative techniques, with a focus on long conversations. Furthermore, we propose CLAIM, a two-stage, Intent-driven Multi-agent framework designed to enhance manipulation analysis by enabling context-aware and informed decision-making. Our results highlight the potential of incorporating agentic frameworks to improve fairness and transparency in judicial processes. We hope that this contributes to the broader application of NLP in legal discourse analysis and the development of robust tools to support fairness in legal decision-making. Our code and data are available at https://github.com/Disha1001/CLAIM.

• The paper introduces CLAIM, a multi-agent framework that extracts speaker intentions and identifies manipulative dialogue segments.
• It employs a hybrid Intent-Driven Chain-of-Thought prompting with specialized agents to improve detection performance.
• The framework leverages the LegalCon dataset of 1,063 annotated courtroom dialogues to advance fairness and transparency in legal analyses.

Analyzing Manipulation in Courtroom Dialogues: A Multi-Agent Framework

The paper "CLAIM: An Intent-Driven Multi-Agent Framework for Analyzing Manipulation in Courtroom Dialogues" introduces a sophisticated approach to understanding manipulation in legal discourse, an area traditionally under-explored by modern computational linguistics. This work presents both a novel dataset, LegalCon, and a two-step analytical framework, CLAIM, specifically aimed at dissecting manipulative practices within courtroom environments using advanced NLP techniques.

Courtroom dialogues inherently encompass strategic manipulation, where language can shape perceptions and affect judicial outcomes. Addressing the pivotal need to identify and understand these manipulative tactics, the authors constructed the LegalCon dataset comprising 1,063 annotated conversations. These dialogues were sourced from diverse judicial contexts, including real court proceedings and legally thematic television shows, ensuring a broad representation of potential courtroom manipulation scenarios. LegalCon is annotated for manipulation presence, primary manipulators, and specific manipulative techniques, offering a significant resource for researchers in computational law and NLP.

The CLAIM framework employs a hybrid methodology integrating Intent-Driven Chain-of-Thought (CoT) prompting with a Multi-Agent Framework. This multi-stage process involves first extracting speaker intentions, followed by utilizing a team of specialized agents tasked with different facets of manipulation analysis. The agents work collaboratively, each processing aspects of detected manipulation—from identifying tactics used to reasoning how intent influences dialogue—to improve accuracy and insight extraction.

Experimental results, benchmarked against standard prompting methodologies, demonstrate significant advancements with CLAIM in identifying manipulation in courtroom settings. Results show improved performance metrics in detecting manipulative dialogue segments and identifying primary manipulators, underscoring the framework's capacity to handle complex, intention-laden conversations better than traditional models. Specifically, CLAIM's superior results in pinpointing primary manipulators and manipulative techniques offer a promising step towards enhancing transparency and accountability in judicial processes.

The implications of these findings are notable both practically and theoretically. Practically, CLAIM's approach could serve as a foundational tool in developing automated systems to support legal practitioners, offering insights into potential biases and manipulative practices within courtroom exchanges. Theoretically, the framework provides valuable contributions to our understanding of linguistics and manipulation within adversarial settings, highlighting areas for further exploration in the interplay between language, psychology, and law.

Looking ahead, the extension of this work could involve expanding the LegalCon dataset to encompass multilingual transcripts or explore cross-cultural aspects of legal manipulation. Moreover, the application of multi-modal analysis, incorporating audio-visual elements of courtroom interactions, could yield deeper insights. As the nexus of AI and law continues to evolve, frameworks like CLAIM will be pivotal in promoting fair legal processes and informed decision-making.

In conclusion, this paper offers a comprehensive and methodologically sound contribution to NLP and legal studies, providing a critical tool for advancing the fairness and transparency of legal systems. The potential for future developments stemming from this research indicates promising avenues for the broader application of AI in understanding and improving judicial dialogues.