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Artificial intelligence for low income countries

Author

Listed:
  • Muhammad Salar Khan

    (Rochester Institute of Technology)

  • Hamza Umer

    (Hitotsubashi University)

  • Farhana Faruqe

    (University of Virginia)

Abstract

The global adoption rate of artificial intelligence (AI) is rising, indicating its transformative potential. However, this adoption is far from uniform, with low-income countries (LICs) trailing behind significantly. Despite needing AI for development, LICs face multiple challenges in harnessing its benefits, exacerbating existing global disparities in technology adoption. In spite of the potentially important role that AI can play in the development of LICs, AI literature overlooks these countries, with research predominantly focused on more advanced economies. This lack of inclusivity contradicts the principles of distributive justice and global equity, prompting us to explore the importance of AI for LICs, offer a theoretical grounding for AI catch-up, identify effective AI domains, and propose strategies to bridge the AI gap. Drawing insights from the leapfrogging and absorptive capacities literature, our position paper presents the feasibility of AI catch-up in LICs. One crucial finding is that there is no one-size-fits-all approach to achieving AI catch-up. LICs with strong foundations could favor leapfrogging strategies, while those lacking such foundations might find learning and acquisition prescriptions from absorptive capacity literature more relevant. The article also makes policy recommendations that advocate for the swift integration of AI into critical LIC domains such as health, education, energy, and governance. While LICs must address challenges related to digital infrastructure, human capital, institutional robustness, and effective policymaking, among others, we believe that advanced AI economies and relevant international organizations like UNESCO, OECD, USAID, and the World Bank can support LICs in AI catch-up through tech transfer, grants, and assistance. Overall, our work envisions global AI use that effectively bridges development and innovation disparities.

Suggested Citation

  • Muhammad Salar Khan & Hamza Umer & Farhana Faruqe, 2024. "Artificial intelligence for low income countries," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    • Handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-03947-w
    DOI: 10.1057/s41599-024-03947-w
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    References listed on IDEAS

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