Rachel Adams, Fola Adeleke, Leah Junck, Ayantola Alayande, Aarushi Gupta, Urvashi Aneja, Samuel Segun, Rosalind Parkes-Ratanshi, Selam Abdella, Mark Gaffley, Scott Mahoney, Rirhandzu Makamu, Nana Eghele Adade, Liping Bian, Timothy Kintu, Atwine Mugume, Aline Germani, Michelle El Kawak, Bheeshma Patel, Olanrewaju Lawa, Sara Khalid, Olubayo Adekanmbi, Rasheedat Sikiru, Toyib Ogunremi, Farhan Yusuf, Hanna Minaye, Imo Etuk, Jimmy Nsenga, Uma Urs, Marzia Zaman, Khondaker A Mamun, Vivian Resende, Pedro Henrique Faria Silva Trocoli-Couto, Rositsa Zaimova, Mamadou Alpha Diallo, Nana Kofi Quakyi, Xiao Fan Liu, Daudi Jjingo, Imad Elhajj, Joyce Nakatumba-Nabende, Tamlyn Eslie Roman, Maryam Mustafa, Brenda Hendry, Yogesh Hooda, Chinazo Anebelundu, Bishesh Khanal, Faisal Sultan, Nirmal Ravi, Darlington Akogo, Zameer Brey, Dave Cohen, Joshua Proctor, Essa Mohamedali, Nneka Mobisson, Amelia Taylor, Joao Archegas, Amrita Mahale, Neal Lesh, Enrica Duncan, Theofrida J Maginga, Hugo Manuel Paz Morales, Henrique Dias Pereira Dos Santos, Tue Vo, Trang Th Nguyen, Robert Korom, Michael Leventhal, Shashi Jain, Livia Maria de Oliveira Ciabati, Praveen Devarsetty, Jane Hirst, Ankita Sharma, Moinul Chowdhury, Henrique Araujo Lima, Caroline Govathson, Sarah Morris
{"title":"Mapping the potential and limitations of using generative AI technologies to address socio-economic challenges in LMICs.","authors":"Rachel Adams, Fola Adeleke, Leah Junck, Ayantola Alayande, Aarushi Gupta, Urvashi Aneja, Samuel Segun, Rosalind Parkes-Ratanshi, Selam Abdella, Mark Gaffley, Scott Mahoney, Rirhandzu Makamu, Nana Eghele Adade, Liping Bian, Timothy Kintu, Atwine Mugume, Aline Germani, Michelle El Kawak, Bheeshma Patel, Olanrewaju Lawa, Sara Khalid, Olubayo Adekanmbi, Rasheedat Sikiru, Toyib Ogunremi, Farhan Yusuf, Hanna Minaye, Imo Etuk, Jimmy Nsenga, Uma Urs, Marzia Zaman, Khondaker A Mamun, Vivian Resende, Pedro Henrique Faria Silva Trocoli-Couto, Rositsa Zaimova, Mamadou Alpha Diallo, Nana Kofi Quakyi, Xiao Fan Liu, Daudi Jjingo, Imad Elhajj, Joyce Nakatumba-Nabende, Tamlyn Eslie Roman, Maryam Mustafa, Brenda Hendry, Yogesh Hooda, Chinazo Anebelundu, Bishesh Khanal, Faisal Sultan, Nirmal Ravi, Darlington Akogo, Zameer Brey, Dave Cohen, Joshua Proctor, Essa Mohamedali, Nneka Mobisson, Amelia Taylor, Joao Archegas, Amrita Mahale, Neal Lesh, Enrica Duncan, Theofrida J Maginga, Hugo Manuel Paz Morales, Henrique Dias Pereira Dos Santos, Tue Vo, Trang Th Nguyen, Robert Korom, Michael Leventhal, Shashi Jain, Livia Maria de Oliveira Ciabati, Praveen Devarsetty, Jane Hirst, Ankita Sharma, Moinul Chowdhury, Henrique Araujo Lima, Caroline Govathson, Sarah Morris","doi":"10.1038/s43588-026-00960-8","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":18.3000,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature computational science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1038/s43588-026-00960-8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
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.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
