Developing countries are being left behind within the AI race – and that’s an issue for all of us

Artificial Intelligence (AI) is far more than simply a buzzword nowadays. It powers facial recognition in smartphones and computers, translation between foreign languages, systems which filter spam emails and discover toxic content on social media, and may even detect cancerous tumours. These examples, together with countless other existing and emerging applications of AI, help make people’s day by day lives easier, especially within the developed world.

As of October 2021, 44 countries were reported to have their very own national AI strategic plans, showing their willingness to forge ahead in the worldwide AI race. These include emerging economies like China and India, that are leading the best way in constructing national AI plans inside the developing world.

Oxford Insights, a consultancy firm that advises organisations and governments on matters referring to digital transformation, has ranked the preparedness of 160 countries internationally in the case of using AI in public services. The US ranks first of their 2021 Government AI Readiness Index, followed by Singapore and the UK.

Notably, the lowest-scoring regions on this index include much of the developing world, resembling sub-Saharan Africa, the Carribean and Latin America, in addition to some central and south Asian countries.

The developed world has an inevitable edge in making rapid progress within the AI revolution. With greater economic capability, these wealthier countries are naturally best positioned to make large investments within the research and development needed for creating modern AI models.

In contrast, developing countries often have more urgent priorities, resembling education, sanitation, healthcare and feeding the population, which override any significant investment in digital transformation. In this climate, AI could widen the digital divide that already exists between developed and developing countries.

The hidden costs of recent AI

AI is traditionally defined as “the science and engineering of creating intelligent machines”. To solve problems and perform tasks, AI models generally have a look at past information and learn rules for making predictions based on unique patterns in the info.

AI is a broad term, comprising two important areas – machine learning and deep learning. While machine learning tends to be suitable when learning from smaller, well-organised datasets, deep learning algorithms are more suited to complex, real-world problems – for instance, predicting respiratory diseases using chest X-ray images.

Many modern AI-driven applications, from the Google translate feature to robot-assisted surgical procedures, leverage deep neural networks. These are a special style of deep learning model loosely based on the architecture of the human brain.

Crucially, neural networks are data hungry, often requiring tens of millions of examples to learn find out how to perform a brand new task well. This means they require a fancy infrastructure of knowledge storage and modern computing hardware, in comparison with simpler machine learning models. Such large-scale computing infrastructure is usually unaffordable for developing nations.

Beyond the hefty price tag, one other issue that disproportionately affects developing countries is the growing toll this type of AI takes on the environment. For example, a up to date neural network costs upwards of US$150,000 to coach, and can create around 650kg of carbon emissions during training (comparable to a trans-American flight). Training a more advanced model can result in roughly five times the whole carbon emissions generated by a median automobile during its entire lifetime.

Developed countries have historically been the leading contributors to rising carbon emissions, however the burden of such emissions unfortunately lands most heavily on developing nations. The global south generally suffers disproportionate environmental crises, resembling extreme weather, droughts, floods and pollution, partially due to its limited capability to speculate in climate motion.

Developing countries also profit the least from the advances in AI and all the great it may bring – including constructing resilience against natural disasters.

Using AI for good

While the developed world is making rapid technological progress, the developing world appears to be underrepresented within the AI revolution. And beyond inequitable growth, the developing world is probably going bearing the brunt of the environmental consequences that modern AI models, mostly deployed within the developed world, create.

But it’s not all bad news. According to a 2020 study, AI may also help achieve 79% of the targets inside the sustainable development goals. For example, AI could possibly be used to measure and predict the presence of contamination in water supplies, thereby improving water quality monitoring processes. This in turn could increase access to clean water in developing countries.

The advantages of AI in the worldwide south could possibly be vast – from improving sanitation, to helping with education, to providing higher 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 wish to realize the true value of “good AI”, equitable participation in the event and use of the technology is important. This means the developed world needs to supply greater financial and technological support to the developing world within the AI revolution. This support will must be greater than short term, but it’ll create significant and lasting advantages for all.