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Developing AI-Informed Vulnerability Index to Target Aid
Eligibility and Distribution for
the Food Security Program in Zambia
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Image by SpaceX
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In response to surging food insecurity in the wake of COVID-19, the Zambian government has tripled the Food Security Pack (FSP) to the vulnerable population. This research project aims to develop an AI-informed vulnerability index to assess food insecurity within communities in Zambia to shape aid distribution. This study focuses on generating robust wealth estimates at a granular level, combining satellite imagery, social media, and spatial analysis. This research is expected to have a high global impact by offering enhanced data for key development actors to reach people and regions with extreme poverty. The project's outputs, such as high-resolution poverty maps, will be shared with Innovation for Poverty Action and the Ministry of Community Development and Social Services to expand the FSP programs. These maps can be updated as new data becomes available to decision-makers in near real time.
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NUMBER OF RECIPIENTS FOR THE FOOD SECURITY PACK
In response to increasing food insecurity, exacerbated by COVID-19, Zambia is tripling the reach of its Food Security Program.
Research
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Dr. Jung's latest research project aims to develop a multimodal approach that combines daytime satellite imagery and social media to estimate the wealth and livelihood of regions and shape aid distribution.
PARTNERS ....................................................
Recipient of Microsoft's
AI for Humanitarian Action Project Initiatives' $25,000 Azure Grant.
PARTNERS ....................................................
PARTNERS ....................................................
Recipient of the Research Council's Collaborative Multidisciplinary Award
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Close up Portrait
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Each year, billions of dollars are allocated internationally to alleviate poverty. Our work can contribute globally by offering novel sources of data for Zambia and neighboring countries to expand their social protection programs. After this project validates its performance in Zambia, we can apply the model to the Republic of the Congo, a neighboring country without the Demographic and Health Surveys (DHS) wealth data and in an even more challenging data environment.
RESEARCH TEAM .................................................................
The team consists of computer/data scientists
and social scientists:
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Woojin Jung (PI) - Rutgers, School of Social Work
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Dmitris Metaxas - Rutgers, Computer Science
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Quentin Stoeffler - University of Bordeaux, Economics
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Saeed Ghadimi - University of Waterloo, AI Institute
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Dimitrios Ntarlagiannis - Rutgers, Earth and Env. Sciences
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Arunesh Sinha - Rutgers, Mgmt Science and Information Systems
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Min Xu - Rutgers, Statistics
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Tawfiq Ammari - Rutgers, Communication and Information
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Andrew Kim - Rutgers, School of Social Work
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Maryam Hosseini - MIT, Urban Studies and Planning
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Melissa Sartorius - Rutgers, School of Social Work
Research Duration
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July 2021 - Ongoing
The study will conclude with the dissemination of findings to policymakers.
3 STAGES ................................................
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Assess the current machine learning model
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Develop new algorithms with a wider application
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Share this new field-tested and evaluated approach with policymakers
RESEARCH TEAM .................................................................
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Sajedeh Goudarzi - Rutgers, Global Urban Studies
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William Benjamin - Allstate Corp., Data Science
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Rofaida Benotsmane - Istanbul Technical Univ., Economics
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Yuxiao Lu - Singapore Management Univ., Computer Science
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Vatsal Shah - Rutgers, Computer Science, Statistics
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EJ Knopf - Rutgers, School of Social Work
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Jamie Cramer - Rutgers, School of Social Work
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Tamara Billima - Local NGO
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Bernard Tembo - Local NGO
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Owen Siyoto - Local NGO
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Cheelo Mwiinga - Local NGO
The research team represents diversity across disciplines, countries/continents (Africa, Asia, US), languages, and cultures. Our study can contribute to broadening the reach of social protection programs by identifying vulnerable and poor populations in the Republic of the Congo and other countries where there is very limited, georeferenced wealth data.
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