AI for Social Good: A technological nudge for development

By Dr. Ingmar Weber, Research Director, Social Computing, Qatar Computing Research Institute, and Jennifer Colville, Innovation Team Lead, Arab States, United Nations Development Programme


Are you an optimist or a pessimist when it comes to Artificial Intelligence? Do you think AI will bring apocalyptic scenarios where the disruptive forces of technology take over the world and make us humans redundant or the servants of technology? Or do you think AI presents opportunities for solving the world’s most pressing challenges?

Pessimist point of view: “Extreme poverty won’t be solved by algorithms.”

Optimist point of view:  “This Twitter sentiment dashboard will change everything.”

We at the Qatar Computing Research Institute (QCRI) and United Nations Development Programme (UNDP) land somewhere in the middle of this spectrum. Bearing in mind the important risks of privacy breaches, ethical missteps, increased inequalities (to name a few), we see AI as an important catalyst for transforming the way we think about development and for achieving sustainable development in the 21st century.

The question of how to harness the power of AI for social good was what we had in mind when we gathered AI experts from around the world for a two-day workshop in Doha, Qatar, 17-18 February 2019.  Together with participants from the private sector, academia, foundations, and international agencies, we set out to bridge the gap between the optimists and the pessimists and to address a set of real-world challenges that can be tackled using AI. Our goal was also to build new connections among participants in order to facilitate information flow across sectors and lasting future collaboration.

The workshop consisted of two components: 1) a series of presentations that demonstrated the latest research in the area of AI for Social Good, for example, how UN agencies and their national partners use data generated from social media platforms for poverty mapping and how international NGOs/foundations use satellite data to understand the characteristics and needs of displaced people.  Links to presentations (slides and videos) can be found here.  2) a hands-on session where those with development and humanitarian challenges met with AI researchers, data owners/providers, and other AI experts to discuss how AI can be used to provide insights into their challenges.

With the input of the AI experts in the room, those with data-centric research challenges were able to identify new types/sources of data, new analytical approaches, and new partnerships to the wicked problems they presented. Importantly, they also considered the risks associated with using AI, taking into account ethical, technical, political and operational issues.

We thank those holding the research challenges for presenting them, and the AI community for their interest and generosity in sharing their expertise. We welcome the global AI community to peruse the research challenges and reach out if you would like to learn more and get involved (contact information below the table).


Research Challenge Presenter Project Name/Description SDG/Topic Possible Partners People To Contact
UNICEF Innovation Poverty Mapping Using Facebook and Satellite Data No poverty (#1) Thinking Machines, QCRI Ingmar Weber,  Vedran Sekara, Isabelle Tingzon
UNDP Lebanon Enhanced monitoring of tensions (and prediction of violence) Peace, Justice and Strong Institutions (#16) Facebook, QCRI Tom Lambert
UNDP Lebanon Cost effectiveness in Data analytics, Data Visualization and Lean Impact Measurement Peace, Justice and Strong Institutions (#16) Dalberg, Thinking Machines Marat Murzabekov
UNDP Lebanon Identifying and reaching out to social referents and/or high risk and at risk people Peace, Justice and Strong Institutions (#16) Facebook Marat Murzabekov
UNDP Lebanon UNDP Live Lebanon. Engaging the Lebanese diaspora in development efforts in rural areas in Lebanon Partnerships for the Goals (#17) Facebook Rawad Rizk
UNDP Sudan Digital microfinance to poor producers Industry, Innovation and Infrastructure (#9) MTN, Vodafone, QCRI, Dalberg Jennifer Colville, Anisha Thapa, John Anodam
UNDP Sudan Strengthen value chains by risk monitoring and info sharing Decent Work and Economic Growth (#8) MTN, Vodafone, QCRI, Dalberg Jennifer Colville, Anisha Thapa, John Anodam
Qatar Red Crescent Data life cycle Data Standardization QCRI, Dalberg Data Insights, Data Aurora, iMMAP Khaled Diab
IOM Data lake for DTM data Migration and Displacement Eduardo Zambrano
UNHCR Estimating integration refugees in host communities & refugee movement Migration and Displacement UN Global Pulse (NY) Rebecca Moreno Jimenez, Miguel Luengo-Oroz
UNHCR Internal surveys text analytics (e.g. comments section) open-source application Migration and Displacement Rebecca Moreno Jimenez
IDMC Estimating magnitude, location and duration of disaster displacement Migration and Displacement Facebook, Vodafone, Telefonica, Thinking Machines Justin Ginnetti, Pedro Rente Lourenco, Andi Gros
IOM Offline data collection Migration and Displacement Eduardo Zambrano
IDMC Forecasting magnitude, location and duration of disaster displacement Migration and Displacement Facebook, Vodafone, Telefonica, Pulse Lab Jakarta Justin Ginnetti, Muhammad Rizal Khaefi
IDMC Detecting incidents of displacement Migration and Displacement QCRI (AIDR) Justin Ginnetti, Muhammad Imran, Freda Ofli, Kareem Darwish and Ahmed Abdelali
IDMC Rapid ground truthing of displacement Migration and Displacement QCRI (AIDR, Micromapper) Justin Ginnetti Muhammad Imran, Ferda Ofli
IDMC Understanding the characteristics and needs of displaced people Migration and Displacement Facebook, iMMAP


Justin Ginnetti
IDMC Building more user-oriented models and decision-support tools (for preparing for/responding to displacement) Migration and Displacement


IDMC, Thinking Machines


Justin Ginnetti, Pedro Rente Lourenc
IDMC Understanding magnitude and drivers of cross-border displacement Migration and Displacement IDMC, Vodafone, iMMAP Justin Ginnetti, Pedro Rente Lourenc
World Bank “Data Fusion”: Robust / Real-time Measurement of labour (and other economic) indicators Decent Work and Economic Growth (#8) Northeastern, Harvard Sam Fraiberger
EAA AI for education and digital education for refugees Quality Education (#4) QCRI, UNICEF Haya Thowfeek, Maleiha Malek
World Bank Migration Patterns in MENA Sustainable Cities and Communities (#11) Sam Fraiberger


A more detailed version of the list of research challenges can be found here.

For further information, please contact Ingmar Weber ( and Jennifer Colville (

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