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Rirhandzu Makamu

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Articles

Mapping the potential and limitations of using generative AI technologies to address socio-economic challenges in LMICs - Nature Computational Science

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By Rachel Adams, Fola Adeleke, Leah Junck, Aarushi Gupta, Urvashi Aneja, Samuel Segun, Rosalind Parkes-Ratanshi, Selam Abdella, Mark Gaffley, Scott Mahoney Verified, Rirhandzu Makamu, Nana Eghele Adade, Liping Bian, Timothy M. Kintu, Atwine Mugume, Aline Germani, Michelle El Kawak, Bheeshma Patel, Olanrewaju Lawa, Sara Khalid, Olubayo Adekanmbi, Rasheedat Sikiru, Toyib Ogunremi, Farhan Isak Yusuf, Hanna Minaye, Imo Etuk, Jimmy Nsenga, Uma Urs, Marzia Zaman, Khondaker A. Mamun, Vivian Resende, Rositsa Zaimova, Mamadou Diallo, Nana Kofi Quakyi, Xiao Fan Liu, Daudi Jjingo, Imad H 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 L. Proctor, Essa Mohamedali, Nneka Mobisson, Amelia Taylor, João Victor Archegas, Amrita Mahale, Neal Lesh, Enrica Duncan, Theofrida Maginga, Tue Vo, Trang Nguyen, Trang Nguyễn, Trạng nguyên, Robert R Korom, Michael Leventhal, Shashi Jain, Praveen Devarsetty, Jane Hirst, Ankita Sharma, Moinul Haque Chowdhury, Henrique Lima, Caroline Govathson, Sarah Morris
| Nature Verified
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...
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