Using Large Language Models for Humanitarian Frontline Negotiation: Opportunities and Considerations

Zilin Ma, Susannah Cheng Su, Nathan Zhao, Linn Bieske, Blake Bullwinkel, Yanyi Zhang, Jinglun Gao, Gekai Liao, Siyao Li, Ziqing Luo, Boxiang Wang, Zihan Wen, Yanrui Yang, Claude Bruderlein, Weiwei Pan

Keywords: Large Language Models (LLMs), Humanitarian Negotiations, Artificial Intelligence (AI), AI-assisted Decision Making, AI Safety, AI Ethics, Data Privacy in AI

TL;DR: We demonstrated how advancements in LLMs can enhance frontline humanitarian negotiations decision-making, informed by interviews with practitioners and evaluations of AI-augmented tools, highlighting opportunities and concerns in such application.

Abstract: Humanitarian negotiations in conflict zones, called frontline negotiation, are often highly adversarial, complex, and high-risk. Several best-practices have emerged over the years that help negotiators extract insights from large datasets to navigate nuanced and rapidly evolving scenarios. Recent advances in large language models (LLMs) have sparked interest in the potential for AI to aid decision making in frontline negotiation. Through in-depth interviews with 13 experienced frontline negotiators, we identified their needs for AI-assisted case analysis and creativity support, as well as concerns surrounding confidentiality and model bias. We further explored the potential for AI augmentation of three standard tools used in frontline negotiation planning. We evaluated the quality and stability of our ChatGPT-based negotiation tools in the context of two real cases. Our findings highlight the potential for LLMs to enhance humanitarian negotiations and underscore the need for careful ethical and practical considerations.

Submission Number: 59