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Supporting peace negotiations in the Yemen war through machine learning
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Arana-Catania, Miguel, van Lier, Felix-Anselm and Procter, Rob (2022) Supporting peace negotiations in the Yemen war through machine learning. Data and Policy, 4 . e28. doi:10.1017/dap.2022.19 ISSN 2632-3249.
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Official URL: https://doi.org/10.1017/dap.2022.19
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 paper also emphasises the importance of interdisciplinary and participatory, co-creation methodology for the development of context-sensitive and targeted tools and to ensure meaningful and responsible implementation.
| Item Type: | Journal Article | ||||||
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| Subjects: | J Political Science > JZ International relations Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
| Library of Congress Subject Headings (LCSH): | Machine learning, Natural language processing (Computer science), Conflict management -- Data processing, Peace-building | ||||||
| Journal or Publication Title: | Data and Policy | ||||||
| Publisher: | Cambridge University Press | ||||||
| ISSN: | 2632-3249 | ||||||
| Official Date: | 2022 | ||||||
| Dates: |
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| Volume: | 4 | ||||||
| Article Number: | e28 | ||||||
| DOI: | 10.1017/dap.2022.19 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 2 August 2022 | ||||||
| Date of first compliant Open Access: | 8 August 2022 | ||||||
| RIOXX Funder/Project Grant: |
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