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The rise of artificial intelligence: what does it mean for development?

Leebong Lee's picture

Video: Artificial intelligence for the SDGs (International Telecommunication Union)

Along with my colleagues on the ICT sector team of the World Bank, I firmly believe that ICTs can play a critical role in supporting development. But I am also aware that professionals on other sector teams may not necessarily share the same enthusiasm.

Typically, there are two arguments against ICTs for development. First, to properly reap the benefits of ICTs, countries need to be equipped with basic communication and other digital service delivery infrastructure, which remains a challenge for many of our low-income clients. Second, we need to be mindful of the growing divide between digital-ready groups vs. the rest of the population, and how it may exacerbate broader socio-economic inequality.

These concerns certainly apply to artificial intelligence (AI), which has recently re-emerged as an exciting frontier of technological innovation. In a nutshell, artificial intelligence is intelligence exhibited by machines. Unlike the several “AI winters” of the past decades, AI technologies really seem to be taking off this time. This may be promising news, but it challenges us to more clearly validate the vision of ICT for development, while incorporating the potential impact of AI.

It is probably too early to figure out whether AI will be blessing or a curse for international development… or perhaps this type of binary framing may not be the best approach. Rather than providing a definite answer, I’d like to share some thoughts on what AI means for ICT and development.

AI and the Vision of ICT for Development

Fundamentally, the vision of ICT for development is rooted in the idea that universal access to information is critical to development. That is why ICT projects at development finance institutions share the ultimate goal of driving down the cost of information. However, we have observed several notable features of the present information age: 1) there is a gigantic amount of data to analyze, which is growing at an unprecedented rate and 2) in the highly complex challenges of our world, it is almost impossible to discover structures in raw data that can be described as simple equations, for example when finding cures for cancer or predicting natural disasters.

This calls for a new powerful tool to convert unstructured information into actionable knowledge, which is expected to be greatly aided by artificial intelligence. For instance, machine learning, one of the fastest-evolving subfields in AI research, provides feature predictions with greatly enhanced accuracies at much lower costs. As an example, we can train a machine with a lot of pictures, so that it can later tell which photos have dogs in it or not, without a human’s prior algorithmic input.

To summarize, AI promises to achieve the vision of ICT for development much more effectively. Then, what are some practical areas of its usage?

AI for development: areas of application

Since AI research is rapidly progressing, it is challenging to get a clear sense of all the different ways AI could be applied to development work in the future; nonetheless, the following are a couple areas where current AI technologies are expected to provide significant added-value.

First, AI allows us to develop innovative new solutions to many complex problems faced by developing countries. As an example, a malaria test traditionally requires a well-trained medical professional who analyzes blood samples under a microscope. In Uganda, an experiment showed that real-time and high-accuracy malaria diagnoses are possible with machines running on low-powered devices such as Android phones.

Secondly, AI could make significant contributions to designing effective development policies by enabling accurate predictions at lower costs. One promising example is the case of the US-based startup called Descartes. The company uses satellite imagery and machine learning to make corn yield forecasts in the US. They use spectral information to measure chlorophyll levels of corn, which is then used to estimate corn production. Their projections have proven to be consistently more accurate than the survey-based estimates used by the US Department of Agriculture. This kind of revolution in prediction has great potential to help developing economies design more effective policies, including for mitigating the impact of natural disasters.
Weekly state and county-level corn prediction by Descartes lab

Looking forward – Toward the democratization of AI?

Many assume that it is too early to talk about AI in the developing world, but the mainstreaming of AI may happen sooner than most people would assume. Years ago, some tech visionaries already envisioned that AI would soon become a commodity like electricity. And this year, Google revealed TensorFlow Lite, the first software of its kind that runs machine learning models on individual smartphones. Further, Google is working on the AutoML project, an initiative to leverage machine learning to automate the process of designing machine learning models themselves.

As always, new technology can be liberating and disruptive, and the outcome will largely depend on our own ability to use it wisely. Despite the uncertainty, AI provides another exciting opportunity for the ICT sector to leverage technological innovation for the benefit of the world’s marginalized populations.

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