Speaker
Description
As artificial intelligence (AI) systems increasingly intersect with multi-stakeholder negotiation processes, understanding their technical reliability and ethical dimensions becomes crucial for sustainable implementation. This study, "Testing the Limits: Technical Validation of AI-Driven Negotiation Tools for Multi-Stakeholder Consensus," examines the technical capabilities of AI negotiation platforms, particularly the AI Stakeholder Negotiation Platform, to mediate and balance conflicting interests across sectors, such as government, civil society, and industry. While these platforms offer potential to support complex negotiations by simulating consensus-building mechanisms and providing real-time recommendations, they raise concerns about transparency, accountability, and contextual adaptability—issues previously highlighted by scholars like Cheong (2024) and Singhal et al. (2024) .
This research aims to advance understanding of technical testing methodologies for AI-driven negotiation tools, emphasizing how technical validation can strengthen reliability and fairness in diverse, high-stakes environments. Employing a case study approach, we will conduct playtesting sessions, collect data on user interactions, and perform simulations to evaluate the AI platform’s performance, including adaptability across varying negotiation contexts and stakeholder dynamics. Semi-structured interviews with platform users will provide insights into perceptions of the AI's support role in negotiations, while technical tests will assess potential biases and adaptability in decision-making scenarios.
The implications of this research are broad, providing actionable insights for AI developers and policymakers. AI can be utilized to enhance collaborative decision-making in multiple areas, including contract negotiations, conflict resolution, political debates, and citizens' assemblies (Tessler et al., 2024). By identifying both limitations and strengths, the study contributes to designing AI negotiation tools that promote equitable and consensus-driven outcomes. Ultimately, this research seeks to establish a robust framework for deploying AI in ethical and effective multi-stakeholder negotiations, supporting an informed path toward AI-mediated consensus in complex, socially significant arenas like environmental sustainability.
| Keywords | Artificial Intelligence (AI); Multi-Stakeholder Negotiation; AI-Driven Negotiation Tools; Technical Validation |
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