Artificial Intelligence (AI) is much more than a buzzword now. It powers facial recognition in smartphones and computers, translation between other languages, spam email filtering, social media toxic content detection, and even cancer tumour diagnosis. These examples, along with a plethora of other existing and emerging AI applications, assist to make people’s life easier, particularly in developed countries.
As of October 2021, 44 countries were said to have their own national AI strategic plans in place, demonstrating their desire to lead the global AI race. Emerging economies such as China and India are leading the charge in constructing national AI programmes in the developing world.
Oxford Insights, a consultancy that helps businesses and governments on digital transformation, has rated the readiness of 160 nations around the world to use artificial intelligence in public services. The United States is ranked first in their Government AI Readiness Index for 2021, followed by Singapore and the United Kingdom.
Much of the developing world, such as Sub-Saharan Africa, the Caribbean, and Latin America, as well as some central and south Asian countries, are among the lowest-scoring regions in this index.
The developed countries will always have an advantage in making great growth in the AI revolution. These wealthier countries are inherently better positioned to make big investments in the research and development needed to create modern AI models due to their larger economic capabilities.
In contrast, developing countries frequently prioritise more pressing issues such as education, sanitation, healthcare, and food production, which generally take priority over any meaningful investment in digital transformation. In this climate, AI has the potential to widen the already-existing digital divide between developed and developing countries.
Hidden cost of Modern AI
Traditional definitions of AI include “the science and engineering of building intelligent machines.” AI models look at the previous data and learn rules for making predictions based on unique patterns in the data to solve issues and perform tasks.
Artificial intelligence (AI) is a broad term that includes two primary areas: machine learning and deep learning. Machine learning algorithms are more adapted to learning from smaller, well-organized datasets, whereas deep learning algorithms are better suited to complicated, real-world situations, such as diagnosing respiratory disorders from chest X-ray images.
Deep neural networks are used in many modern AI-driven applications, from Google Translate to robot-assisted surgical procedures. These are a form of deep learning model that is loosely based on the human brain’s architecture.
Crucially, neural networks are data hungry, often requiring millions of examples to learn how to perform a new task well. This means they require a complex infrastructure of data storage and modern computing hardware, compared to simpler machine learning models. Such large-scale computing infrastructure is generally unaffordable for developing nations.
Beyond the hefty price tag, another issue that disproportionately affects developing countries is the growing toll this kind of AI takes on the environment. For example, a contemporary neural network costs upwards of US$150,000 to train, and will create around 650kg of carbon emissions during training (comparable to a trans-American flight). Training a more advanced model can lead to roughly five times the total carbon emissions generated by an average car during its entire lifetime.
Developed countries have historically been the leading contributors to rising carbon emissions, but the burden of such emissions unfortunately lands most heavily on developing nations. The global south generally suffers disproportionate environmental crises, such as extreme weather, droughts, floods and pollution, in part because of its limited capacity to invest in climate action.
Developing countries also benefit the least from the advances in AI and all the good it can bring—including building resilience against natural disasters.
Using AI for good
The developing world appears to be underrepresented in the AI revolution, while the developed world is making rapid technological progress. Aside from inequitable growth, the developing world is likely to bear the brunt of the environmental consequences that modern AI models, which are primarily used in the developed world, produce.
But it’s not all doom and gloom. AI, according to a 2020 study, can help meet 79 percent of the sustainable development goals’ ambitions. Artificial intelligence could, for example, be used to measure and predict the presence of contamination in water supplies, thereby improving water quality monitoring processes. As a result, developing countries may have easier access to clean water.
The benefits of AI in the global south could be vast—from improving sanitation, to helping with education, to providing better medical care. These incremental changes could have significant flow-on effects. For example, improved sanitation and health services in developing countries could help avert outbreaks of disease.
But if we want to achieve the true value of “good AI”, equitable participation in the development and use of the technology is essential. This means the developed world needs to provide greater financial and technological support to the developing world in the AI revolution. This support will need to be more than short term, but it will create significant and lasting benefits for all.
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