Tackling One Health Risks: How Large Language Models are leveraged for Risk Negotiation and Consensus-building

Tackling One Health Risks: How Large Language Models are leveraged for Risk Negotiation and Consensus - building. Study Centre for Land-use related Evaluation procedures, One-Health, German Federal Institute for Risk Assessment, Berlin, Germany; Email: Maciej.Durkalec@bfr.bund.de Faculty of Bioscience Engineering, Department. of Food Technology, Safety and Health, Ghent University, Ghent, Belgium, E - mail: Andreja.Rajkovic@UGent.be Abstract Key global challenges of our times are characterized by complex interdependencies and can only be effectively addressed through an integrated, participatory effort. Conventional risk analysis frameworks often reduce complexity to ensure manageability, crea ting silos that hinder comprehensive solutions. A fundamental shift towards holistic strategies is essential to enable effective negotiations between different sectors and to balance the competing interests of stakeholders. However, achieving this balance is often hindered by limited time, vast amounts of information, and the complexity of integrating diverse perspectives. This study presents an AI - assisted negotiation framework that incorporates large language models (LLMs) and AI - based autonomous agents i nto a negotiation - centered risk analysis workflow. The framework enables stakeholders to simulate negotiations, systematically model dynamics, anticipate compromises, and evaluate solution impacts. By leveraging LLMs' semantic analysis capabilities we coul d mitigate information overload and augment decision - making process under time constraints. Proof - of - concept implementations were conducted in two real - world scenarios: (i) prudent use of a biopesticide, and (ii) targeted wild animal population control. Ou r work demonstrates the potential of AI - assisted negotiation to address the current lack of tools for cross - sectoral engagement.