Advancing early warning mechanisms: an augmented intelligence-driven model for predicting multidimensional vulnerability levels in Afghanistan
The complex socioeconomic landscape of conflict zones demands innovative approaches to assess and predict vulnerabilities for crafting and implementing effective policies by the United Nations (UN) institutions. This article presents a groundbreaking Augmented Intelligence-driven Prediction Model de...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2025-01-01
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| Series: | Data & Policy |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2632324924000919/type/journal_article |
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| author | Damla Aras Timothy Westlake |
| author_facet | Damla Aras Timothy Westlake |
| author_sort | Damla Aras |
| collection | DOAJ |
| description | The complex socioeconomic landscape of conflict zones demands innovative approaches to assess and predict vulnerabilities for crafting and implementing effective policies by the United Nations (UN) institutions. This article presents a groundbreaking Augmented Intelligence-driven Prediction Model developed to forecast multidimensional vulnerability levels (MVLs) across Afghanistan. Leveraging a symbiotic fusion of human expertise and machine capabilities (e.g., artificial intelligence), the model demonstrates a predictive accuracy ranging between 70% and 80%. This research not only contributes to enhancing the UN Early Warning (EW) Mechanisms but also underscores the potential of augmented intelligence in addressing intricate challenges in conflict-ridden regions. This article outlines the use of augmented intelligence methodology applied to a use case to predict MVLs in Afghanistan. It discusses the key findings of the pilot project, and further proposes a holistic platform to enhance policy decisions through augmented intelligence, including an EW mechanism to significantly improve EW processes, thereby supporting decision-makers in formulating effective policies and fostering sustainable development within the UN. |
| format | Article |
| id | doaj-art-80ad1cdee788468abf2b5a144be15e95 |
| institution | DOAJ |
| issn | 2632-3249 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Data & Policy |
| spelling | doaj-art-80ad1cdee788468abf2b5a144be15e952025-08-20T03:18:26ZengCambridge University PressData & Policy2632-32492025-01-01710.1017/dap.2024.91Advancing early warning mechanisms: an augmented intelligence-driven model for predicting multidimensional vulnerability levels in AfghanistanDamla Aras0https://orcid.org/0009-0009-5032-6619Timothy Westlake1Joint Analysis and Reporting Section, United Nations Assistance Mission in Afghanistan, Kabul, AfghanistanDepartment of Humanities and Digital Sciences, Tilburg University, Tilburg, The Netherlands Department of Data Science, AI and Robotics, Ministry of Defence of the Netherlands, Apeldoorn, The NetherlandsThe complex socioeconomic landscape of conflict zones demands innovative approaches to assess and predict vulnerabilities for crafting and implementing effective policies by the United Nations (UN) institutions. This article presents a groundbreaking Augmented Intelligence-driven Prediction Model developed to forecast multidimensional vulnerability levels (MVLs) across Afghanistan. Leveraging a symbiotic fusion of human expertise and machine capabilities (e.g., artificial intelligence), the model demonstrates a predictive accuracy ranging between 70% and 80%. This research not only contributes to enhancing the UN Early Warning (EW) Mechanisms but also underscores the potential of augmented intelligence in addressing intricate challenges in conflict-ridden regions. This article outlines the use of augmented intelligence methodology applied to a use case to predict MVLs in Afghanistan. It discusses the key findings of the pilot project, and further proposes a holistic platform to enhance policy decisions through augmented intelligence, including an EW mechanism to significantly improve EW processes, thereby supporting decision-makers in formulating effective policies and fostering sustainable development within the UN.https://www.cambridge.org/core/product/identifier/S2632324924000919/type/journal_article |
| spellingShingle | Damla Aras Timothy Westlake Advancing early warning mechanisms: an augmented intelligence-driven model for predicting multidimensional vulnerability levels in Afghanistan Data & Policy |
| title | Advancing early warning mechanisms: an augmented intelligence-driven model for predicting multidimensional vulnerability levels in Afghanistan |
| title_full | Advancing early warning mechanisms: an augmented intelligence-driven model for predicting multidimensional vulnerability levels in Afghanistan |
| title_fullStr | Advancing early warning mechanisms: an augmented intelligence-driven model for predicting multidimensional vulnerability levels in Afghanistan |
| title_full_unstemmed | Advancing early warning mechanisms: an augmented intelligence-driven model for predicting multidimensional vulnerability levels in Afghanistan |
| title_short | Advancing early warning mechanisms: an augmented intelligence-driven model for predicting multidimensional vulnerability levels in Afghanistan |
| title_sort | advancing early warning mechanisms an augmented intelligence driven model for predicting multidimensional vulnerability levels in afghanistan |
| url | https://www.cambridge.org/core/product/identifier/S2632324924000919/type/journal_article |
| work_keys_str_mv | AT damlaaras advancingearlywarningmechanismsanaugmentedintelligencedrivenmodelforpredictingmultidimensionalvulnerabilitylevelsinafghanistan AT timothywestlake advancingearlywarningmechanismsanaugmentedintelligencedrivenmodelforpredictingmultidimensionalvulnerabilitylevelsinafghanistan |