AI And The Developing World: Using AI To Promote Development And Solve Global Challenges

Over the past decade, the Global South has adopted AI tools to address traditional development challenges, and there exists a diverse array of use cases for AI among member countries in the Global South, particularly in agriculture, healthcare, and education. Within agriculture, projects have focused on identifying banana diseases to support farmers in developing countries, building a deep learning object detection model to aid in-field diagnosis of cassava disease in East Africa, and developing imagery observing systems to support precision agriculture and forest monitoring in Brazil. In healthcare, projects have focused on building predictive models to keep expecting mothers in rural India engaged in telehealth outreach programs, developing clinical decision support tools to combat antimicrobial resistance in Ghana, and using AI models to interpret fetal ultrasounds in Zambia. In education, projects have focused on identifying at-risk students in Colombia, enhancing English learning for Thai students, and developing teaching assistants to aid science education in West Africa. There is much anticipation for an increase in AI innovation over the next decade as companies, governments, and various organizations actively work to expand their development.

The emergence of AI within the Global South has also provided opportunities to democratize current AI practices, lead towards the development of more inclusive AI systems, and increase participation from communities underrepresented in AI development. Grassroots organizations, such as Masakhane and Ghana NLP, have emerged to focus on developing datasets and machine translation tools to help expand access to low-resource African languages. Efforts such as Deep Learning Indaba, Khipu, AI Saturdays Lagos, and Data Science Africa, amongst many others, have been instrumental in growing communities of AI researchers and developers within Africa and Latin America by hosting conferences and workshops and helping build local expertise in emerging technology. Many of these issues were recently raised in a Brookings webinar that included leaders from civil society organizations in the Global South, who helped to frame productive dialogues around these and other issues. As calls for localized development of AI systems increase, these organizations will become even more critical in ensuring that AI development meets the needs and interests of local communities.

Large tech companies have continued to expand their footprint in the Global South by establishing research labs, development centers, and engineering offices. IBM was one of the first big tech companies to establish an industry research lab in the Global South, building IBM Research India in 1998. Since then, IBM has launched research labs in São Paulo and Rio de Janeiro (2010), Nairobi (2013), and Johannesburg (2016). India has continued to be a market entry point for big tech research labs in the Global South, with Microsoft opening Microsoft Research India in Bangalore in 2005. Microsoft has continued to expand its reach, opening its Africa Development Center with two locations in Nairobi and Lagos in 2019 and launching the Microsoft Africa Research Institute, also in Nairobi in 2020. Google has also built up its research presence in the Global South, launching an AI research lab in Accra in 2018 and Bangalore in 2019. While developing research labs in the Global South is one step in advancing progress within AI, it is also important to understand that there is significant infrastructure and human capital necessary to maintain these labs and develop broader AI ecosystems.