AI for development: facts and figures

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Agriculture is seeing an explosion of AI-based applications, aimed at helping farmers tackle crop disease, pests and climate-related problems. Copyright: United Soybean Board (CC BY 2.0). This image has been cropped.

Speed read

  • AI offers solutions for agriculture, health and education, and global economy gains
  • But slow uptake in developing countries means benefits for Global South remain ‘modest’
  • Connectivity is a major challenge as half of the global population lacks internet access

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Ruth Douglas explores the application of AI in international development and the risks associated with this rapidly growing technology.

Artificial intelligence (AI) is already being applied to every sector of international development from agriculture, health and education, to urban planning. And there is a groundswell of interest in harnessing this technology for social good.

Farmers are able to mitigate crop failure thanks to AI-based smartphone apps, health authorities can predict the next disease outbreak, and the world’s schools are being mapped to identify gaps in resources.

Governments and corporations are attempting to tap AI technology to predict and monitor natural disasters such as earthquakes, floods and droughts and to target emergency response effectively.

Yet the economic gains from AI in the next decade are expected to be relatively low in developing countries. According to recent predictions, global GDP will be up to 14 per cent higher in 2030 as a result of the accelerating development and take-up of AI — boosting the world economy by US$15.7 trillion.

The figure comes from a report on the value of AI by accounting firm PricewaterhouseCoopers (PwC), which finds that China and North America are likely to see the biggest economic gains, accounting for 70 per cent of the global total. But although it predicts that all economies will benefit, developing countries will experience “more modest increases” due to lower rates of adoption of AI technologies.

National strategies

While many countries in the Global North have adopted national strategies for AI in recent years, Global South countries have been slower off the mark. India, Kenya and Mexico are the only ones which have put such frameworks in place, according to an overview of national AI strategies published in Politics+AI.

The Kenyan government created a taskforce in 2018 to focus on AI and distributed ledger technology such as blockchain, and set out a roadmap for promoting these technologies in the next five years.

India’s AI strategy focuses on social growth and inclusion, and was launched with the hashtag #AIforall in June 2018. It aims to equip Indians with skills to work in the sector, as well as invest in AI research, and export Indian-made AI solutions to other developing countries.

However, despite a lack of national impetus in many nations, AI applications already abound which have the potential to improve lives and livelihoods in the Global South.

AI for agriculture

Agriculture is one sector that is seeing an explosion of AI-based applications, which are aimed at helping farmers tackle crop disease, pests and climate-related problems.

A new global industry report on AI in agriculture says the market size will grow from US$522.6 million in 2017 to US$1,765.98 million by 2023. It cites population growth and subsequent rising demand for agricultural production as a major factor in that growth.

With the global population projected to grow from 7.6 billion in 2018 to more than 9.6 billion in 2050, according to UN figures, there will be a significant increase in the demand for food. Meanwhile, natural resources such as water and fertile agricultural land are coming under increasing pressure from climate change and overuse, fuelling the need for new technologies to improve climate resilience.

Digital innovations and technologies could be part of the solution, according to the Food and Agriculture Organization (FAO). AI technologies, such as machine learning and predictive analytics, allow farmers to receive insights into weather conditions and soil moisture, crop diseases and pests, to inform their decision making. And a number of NGOs, start-up companies and multilateral agencies are committed to making them accessible to smallholder farmers in developing countries.

Connectivity challenge

But for many of these farmers, living in rural areas, there remains a major obstacle – connectivity. And the same is true for health workers and others on the front line of development.

On the one hand, mobile phones have become ubiquitous: a 2016 World Bank report titled Digital Dividends found that, on average, 8 in 10 people in developing countries own a mobile phone and the number is steadily rising. Even among the bottom fifth of the population, nearly 70 per cent own one, it said.

But despite this, “the lives of the majority of the world’s people remain largely untouched by the digital revolution”, the report finds. Nearly 50 per cent of the world’s population has no access to the internet and in developing countries the figure is closer to 40 per cent, according to the UN’s Broadband Commission for Sustainable Development.

This is one of the major challenges faced by AI in the Global South. Connectivity and bandwidth are still limited or unaffordable in many areas, and may be inadequate for AI-based applications.

In low- and middle-income countries, 1GB of data costs more than five per cent of what people earn in a month, well over the “affordable threshold” of 1GB of data priced at 2 per cent or less of average income, according to the Alliance for Affordable Internet. The UN’s Sustainable Development Goals laid out in September 2015 call for universal, affordable internet access by 2020.

Digital literacy and the skills required to develop and maintain sophisticated AI systems are also often lacking in these countries. And AI applications rely on large volumes of data, which may not always be available.

What are the risks?

Added to this, there are concerns over potential inaccuracies of AI software. AI uses data gathered in a particular context. If it is applied elsewhere it might not be relevant or, if applied at a later point in time, may be out of date.

“AI tools should always be locally tailored so that relevant context and sensitivities inform how software developers approach tool design and evaluation,” a USAID report titled Making AI Work for International Development recommends. It calls for “deep local partnerships”, involving technology users, owners and regulators at the design stage, and consulting them early on in the process.

This report also points to one of the most frequently cited risks of AI, both globally and in a development context: the danger that existing societal biases and prejudices could be built into AI programmes. AI models rely on data and this data may already reflect such biases. Or indeed the programme may reflect the inherent biases of its designer. Either way, the result could be the further marginalisation of already vulnerable or excluded sections of the population.

Finally, there is the fear that AI will take people’s jobs. But is this founded? Certainly, AI has the potential to make many jobs redundant as processes become automated and services streamlined. But there is a widely held view that these losses will be compensated for by the creation of new roles in the field.

In a survey of technology business leaders in 108 countries, launched by KPMG and Harvey Nash, respondents typically said they believed around 10 per cent of their company’s workforce will be replaced within five years by AI/automation. However, more than two-thirds said they believed that new jobs will emerge to compensate for this. The dystopian notion of robots taking over from humans remains, for now, a distant threat.