Abstract
Drawing on the experiences and lessons learned from researchers based in low- and middle-income countries (LMICs) that leverage generative artificial intelligence (GenAI) technologies to address socio-economic challenges, we showcase the considerable potential to use GenAI to accelerate the progress towards achieving some of the Sustainable Development Goals, as well as considerable obstacles for creating locally adapted AI tools for fair development in LMICs. An expanded evidence base on GenAI in resource-limited settings is crucial for policymakers to understand opportunities and risks, while rights-based safeguards against AI harms can be strengthened by the lived experiences of local projects.
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Acknowledgements
This research has been supported by the Gates Foundation, and the projects listed and described in this paper have also received funding support from the Gates Foundation.
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R.A., F.A., L.J., A.A., A. Gupta, U.A., S.S., R.P.-R., S.A. and M.G. wrote the paper and coordinated inputs from the other authors. S. Mahoney, R.M., N.E.A., L.B., T.K., A. Mugume, A. Germani, M.E.K., B.P., O.L., S.K., O.A., R.S., T.O., F.Y., H.M., I. Etuk, J.N., U.U., M.Z., K.A.M., V.R., P.H.F.S.T.-C., R.Z., M.A.D., N.K.Q., X.F.L., D.J., I. Elhajj, J.N.-N., T.E.R., M.M., B.H., Y.H., C.A., B.K., F.S., N.R., D.A., Z.B., D.C., J.P., E.M., N.M., A.T., J.A., A. Mahale, N.L., E.D., T.J.M., H.M.P.M., H.D.P.d.S., T.V., T.TH.N., R.K., M.L., S.J., L.M.d.O.C., P.D., J.H., A.S., M.C., H.A.L., C.G. and S. Morris analyzed the projects data that informed the writing of this paper.
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Nature Computational Science thanks Hazel T. Biana and Aditya Vashistha for their contribution to the peer review of this work. Primary Handling Editor: Fernando Chirigati, in collaboration with the Nature Computational Science team.
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Description of the Grand Challenges Equitable AI Use program and a complete list of use cases in Supplementary Tables 1–6.
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Adams, R., Adeleke, F., Junck, L. et al. Mapping the potential and limitations of using generative AI technologies to address socio-economic challenges in LMICs. Nat Comput Sci (2026). https://doi.org/10.1038/s43588-026-00960-8
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DOI: https://doi.org/10.1038/s43588-026-00960-8
