AI in the Global South: Opportunities and Challenges
By Darius Spearman (africanelements)
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AI Development in the Global South
The rise of artificial intelligence, or AI, brings both big chances and tough problems for countries in the Global South. AI can really help improve important areas like farming, healthcare, and education in these regions (brookings.edu). For instance, AI can make farming more precise, helping farmers grow more food with less waste. It can also make healthcare better by helping doctors diagnose illnesses faster or providing medical advice to people in remote areas. Education can also benefit, with AI-powered tools offering personalized learning experiences.
However, there are also worries about how AI is used and the ethical issues it raises. Existing development goals in these countries also present new challenges (brookings.edu). Governments in the Global South need to create strong rules for AI. They also need to build strong AI communities that support new businesses and help people develop skills in this field (brookings.edu). This will help ensure that AI benefits everyone and does not create new problems.
AI Readiness and Disparity
There is a big difference in how ready countries are for AI and how much they have developed it between the Global North and the Global South. Countries in the Global North, like the United States, the United Kingdom, and Germany, are consistently at the top when it comes to AI readiness (muse.jhu.edu). This means they have better access to things like computing power, skilled workers, and relevant data and models (wilsoncenter.org). These resources are often harder to get for researchers and developers in Global South countries (wilsoncenter.org).
The Global South often provides cheap natural resources and undervalued human labor for AI development, instead of being an equal partner (muse.jhu.edu). Workers in the Global South often do repetitive tasks that are important for training AI systems, such as labeling data (muse.jhu.edu). However, their contributions are often not recognized in the global story of AI innovation (muse.jhu.edu). This situation highlights a significant imbalance in the global AI landscape.
Key Factors for AI Readiness
Access to powerful computers and data centers for training and deploying AI models.
A pool of experts and researchers capable of developing and implementing AI technologies.
Availability of high-quality data and pre-trained AI models for various applications.
Inclusive AI Governance
It is very important to have global AI governance that includes the views of the Global South. This will make AI more useful everywhere and ensure fair development. AI technologies that are created with a global perspective, including input from the Global South, are better at solving a wider range of problems and taking advantage of more opportunities (pmc.ncbi.nlm.nih.gov). Nations in the Global South have diverse cultures and unique challenges. They can offer valuable ideas for AI solutions that can be used more broadly (pmc.ncbi.nlm.nih.gov).
When Global South nations are directly involved in AI governance, it can lead to more innovation, better adaptability, and more relevant AI development (pmc.ncbi.nlm.nih.gov). This creates a fairer and more complete AI landscape (pmc.ncbi.nlm.nih.gov). Also, having coordinated global rules and standards is vital. This ensures that AI infrastructure supports goals related to climate, development, and access to energy in emerging markets (institute.global). Inclusive governance helps prevent the concentration of AI power in the Global North and gives developing countries a voice in shaping the future of AI (newamerica.org).
Ethical AI Concerns
The ethical concerns of AI are especially urgent in the Global South. This is because AI has the potential to make existing inequalities even worse (brookings.edu). These concerns include issues with how data is handled, biases in AI algorithms, and the environmental impact of AI. These problems can affect developing countries more severely (brookings.edu). For example, if AI systems are trained on biased data, they can lead to unfair outcomes, such as discrimination in hiring or lending.
Another major ethical issue is the sourcing of materials for AI hardware. Components like cobalt, nickel, and lithium are often mined in resource-rich Global South nations. It is crucial to ensure that these materials are sourced ethically to prevent child labor, dangerous working conditions, and violent conflicts over resources (e-ir.info). Furthermore, the environmental impact of AI, including the massive electricity consumption of data centers, poses an ethical challenge. The Global South may bear the brunt of climate change effects, making it even more important to address AI's carbon footprint (e-ir.info).
Understanding AI Colonialism
AI Colonialism: This term describes how AI development can continue old patterns of colonialism. It means the Global South often provides raw materials, like data and labor, for AI. However, the Global North gets most of the economic benefits. This is similar to how powerful nations exploited colonies in the past.
Data Labeling and Invisible Labor
Data labeling and “invisible labor” in the Global South involve people working to collect, prepare, label, and check data. This work is very important for training AI models (brookings.edu). Because this work takes a lot of effort and can be expensive, tech companies often send these jobs to the Global South. They do this through business process outsourcing (BPO) or online work platforms like Appen and Amazon Mechanical Turk (brookings.edu).
The global market for data annotation, which is a key part of the AI industry, is expected to grow a lot. This is because there is a high demand for labeled datasets, which provide the correct answers for AI to learn from (brookings.edu). However, there are concerns about the working conditions and ethical issues related to this labor. This work can make existing inequalities even worse in the developing countries where it is done (brookings.edu). It is important to ensure fair treatment and ethical practices for these workers.
BRICS Nations and AI
Several BRICS nations and other countries in the Global South are making big steps in developing and using AI. BRICS stands for Brazil, Russia, India, China, and South Africa. China is a leader among the BRICS countries in AI. It accounts for almost half of the world's most-cited AI papers and leads in global AI patent filings (infobrics.org). China's AI strategy is supported by more than $150 billion in government funding. This has led to competitive computer chips and language models from companies like Huawei and Baidu (infobrics.org).
Brazil and South Africa are becoming important centers for ethical AI, especially in farming and public health (infobrics.org). For example, Brazil's Embrapa is using AI in precision farming. This means using AI to make farming more efficient and sustainable, such as monitoring crop health and managing irrigation. South Africa's CSIR is using AI in transportation and eHealth (infobrics.org). eHealth involves using technology to deliver healthcare services, like remote diagnoses. Additionally, Egypt and Ethiopia are investing in smart governance and AI research facilities (infobrics.org). India, a BRICS member, also plays a growing role in global AI discussions, co-chairing the AI Action Summit with France (newamerica.org).
AI Contributions from BRICS Nations
Leads in AI research and patent filings, backed by significant state funding.
Emerging as a hub for ethical AI, particularly in precision farming.
Developing ethical AI applications in public health and transportation.
Investing in smart governance and AI research infrastructure.
AI's Impact on Human Rights
The impact of AI on human rights is complicated. AI can help human rights, for example, by improving healthcare or helping with disaster relief. However, it can also harm them. This can happen through algorithmic bias, which leads to unfair treatment, or surveillance technologies that invade privacy. The exploitation of workers in data annotation, especially in the Global South, is another concern (brookings.edu).
The ethical sourcing of raw materials for AI hardware is also a human rights issue. This is because it can involve child labor and dangerous working conditions (e-ir.info). The growth of AI, along with its data practices and biases, has led to serious concerns and unethical actions. These actions can make existing inequalities worse in the developing countries where this labor is performed (brookings.edu). It is important to address these issues to ensure AI development respects human rights globally.
AI Infrastructure Needs
The Global South needs better AI infrastructure. This mainly means having access to strong computing power and reliable data centers (wilsoncenter.org). These are essential for training and using AI models. However, financial limitations and the fact that most of these resources are in the Global North often mean they are not available in developing countries (newamerica.org).
Data centers, which are a crucial part of AI infrastructure, now use more electricity than entire countries (e-ir.info). This shows how much energy and infrastructure AI needs. The financial limits faced by middle powers in the Global South make it hard for them to build competitive AI systems (newamerica.org). This highlights a lack of sufficient infrastructure. Bridging this gap is vital for the Global South to fully participate in the AI revolution.
Defining the Global South
The term “Global South” generally refers to countries that are often seen as having developing economies. This includes regions like Africa, the Caribbean, Latin America, Southeast Asia, South Asia, and Oceania (brookings.edu). These nations often share a history of colonialism and face challenges related to relying on technology from other countries (e-ir.info). Tech companies often send jobs related to data, such as collecting, preparing, labeling, and checking it, to the Global South. They do this because this work requires a lot of labor and can be costly (brookings.edu).
The power over the AI industry is mostly held by a few rich companies in the Global North. This makes developing countries more dependent on technology from outside (e-ir.info). Middle powers in the Global South see AI through the lens of development and the lasting impact of colonialism. They have money problems that limit their ability to build competitive AI systems (newamerica.org). This means they often struggle to create their own AI technologies and rely on those from wealthier nations.
ABOUT THE AUTHOR
Darius Spearman has been a professor of Black Studies at San Diego City College since 2007. He is the author of several books, including Between The Color Lines: A History of African Americans on the California Frontier Through 1890. You can visit Darius online at africanelements.org.
