Machine Learning for Mediation in Armed Conflicts

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 increasingly ill-equipped to successfully address these challenges. While technology is being increasingly 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 is the first to apply state-of-the-art machine learning technologies to data from an ongoing mediation process. Using dialogue transcripts from peace negotiations in Yemen, this study shows how machine-learning tools can effectively support international mediators by managing knowledge and offering additional conflict analysis tools to assess complex information. Apart from illustrating the potential of machine learning tools in conflict mediation, the paper also emphasises the importance of interdisciplinary and participatory research design for the development of context-sensitive and targeted tools and to ensure meaningful and responsible implementation. Policy Significance Statement This study offers insights into how machine learning tools can be used to assist conflict mediators in organising and analysing data from highly complex and dynamic conflict situations. Machine learning tools can bring significant efficiency improvements to mediation by organising complex data and making it more easily accessible, giving mediators more control over existing information. They can also support moves towards consensus by highlighting areas in which political actors are converging or diverging; point to potentially overlooked areas of conflict or dialogue bottlenecks; and challenge prejudices that may have built up during a mediation process. This study offers a concrete example of how innovative machine learning tools can be used to address mediation in complex, fluid and protracted conflicts.