Supporting peace negotiations in the Yemen war through machine learning. (2nd September 2022)

• Record Type: Journal Article
• Authors: Arana-Catania, Miguel; van Lier, Felix-Anselm; Procter, Rob
• Is Part Of: Data & policy. Volume 4(2022)
• Publication Date: 2022-09-02
• Subjects: conflict mediation -- machine learning -- NLP -- peace-making; Policy sciences -- Periodicals; Policy sciences -- Statistical methods -- Periodicals; Policy sciences -- Data processing -- Periodicals; Decision making -- Data processing -- Periodicals; 320.60727
• DOI: 10.1017/dap.2022.19
• Languages: English
• ISSNs: 2632-3249

Abstract

Abstract: Today's conflicts are becoming increasingly complex, fluid, and fragmented, often involving a host of national and international actors with multiple and often divergent interests. This development poses significant challenges for conflict mediation, as mediators struggle to make sense of conflict dynamics, such as the range of conflict parties and the evolution of their political positions, the distinction between relevant and less relevant actors in peace-making, or the identification of key conflict issues and their interdependence. International peace efforts appear ill-equipped to successfully address these challenges. While technology is already being experimented with and used in a range of conflict related fields, such as conflict predicting or information gathering, less attention has been given to how technology can contribute to conflict mediation. This case study contributes to emerging research on the use of state-of-the-art machine learning technologies and techniques in conflict mediation processes. Using dialogue transcripts from peace negotiations in Yemen, this study shows how machine-learning can effectively support mediating teams by providing them with tools for knowledge management, extraction and conflict analysis. Apart from illustrating the potential of machine learning tools in conflict mediation, the article also emphasizes the importance of interdisciplinary and participatory, cocreation methodology for the development of context-sensitive and targeted tools and to ensure meaningful and responsible implementation.