The crisis is an wake-up call to developing countries to speed up the digitalisation of their economies
In this article, I will refer to current efforts to harness Artificial Intelligence (AI) against COVID-19, note its promises, limitations, and potential pitfalls, and identify priorities for developing countries. Artificial Intelligence (AI) is the use of algorithms, data, and statistics to teach computers to recognize patterns and predict outcomes. Pattern recognition and prediction are what underlies Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision, the main applications of modern AI.
Since the outbreak of the pandemic in December 2019, there has been a rush to harness AI in the fight. I document these in a recent companion article in Towards Data Science on Medium. AI can help track and predict the spread of the infection, it can help make diagnoses and prognoses, and it can search for treatments and a vaccine. It can also be used for social control for instance, to help isolate those that are infected and monitor and enforce compliance with lockdown measures.
Unfortunately, AI is currently not up to the job to rigorously track and predict the infection. It cannot yet provide reliable assistance in diagnoses. And while its most promising use is to search for a vaccine and treatments, these will take a long time. The main reason for this somewhat pessimistic conclusion is inadequate data. The problem in the current crisis is that there is, on the one hand, not suitable enough (that is, unbiased and sufficient) data to train AI models to predict and diagnose COVID-19. Most of the studies that have trained AI models to diagnose COVID-19 from CT scans or X-rays have made use of small, biased, and unrepresentative samples from China. Many of these studies are not (yet) published in peer-reviewed journals.
On the other hand, the global impact and focus on the pandemic have resulted in too much data. There is too much noisy social media data associated with COVID-19, which, as the failure of Google Flu Trends, illustrated more than five years ago. This failure is dissected by Lazer and colleagues in a 2014 paper in Science, in which they identified the noisy social media data as upending big data hubris and algorithm dynamics. These factors currently also bedevil efforts to track COVID-19 using big data from social media. Furthermore, and perhaps more importantly, the systemic shock which the outbreak has caused has led to a deluge of outlier data. In essence, COVID-19 is a massive unique event. This sudden deluge of new data is invalidating almost all prediction models in economics, finance, and business. The consequence is that many industries are going to be pulling the humans back into the forecasting chair that had been taken from them by the models.
So, while we will not likely see AI in prediction and diagnoses during the current COVID-19 pandemic, we are likely to see the growing use of AI for social control. In contrast to AIs limitations in prediction and diagnoses due to data problems, no such problems exist in using surveillance technology. The use of mass surveillance to enforce lockdown and isolation measures in China, including infrared cameras to identify potentially infected persons in public, has been well documented. These have not been limited to China, but are being adopted by many western democracies, including the USA, UK, Germany, and Spain. Here, it is not so much public infrared cameras that are used but rather personal mobile phone data that are being requested by governments.
Moreover, many developing economies are following suit. OneZero has compiled a list of at least 25 countries that by mid-April 2020 had resorted to surveillance technologies to track compliance and enforce social distancing measures. Many of these violate data privacy norms. These include developing countries such as Argentina, Brazil, Ecuador, India, Indonesia, Iran, Kenya, Pakistan, Russia, South Africa, and Thailand. In the case of South Africa, the country is reported to have contracted a Singapore-based AI company to implement a real-time contact tracing and communication system. Singapore is using an app called TraceTogether, which sends out warnings if social distancing limits are breached.
In addition to social control and compliance measuring, AI systems via apps and mobile devices can also help health authorities to manage. According to Petropoulos, these can enable patients to receive real-time waiting-time information from their medical providers, to provide people with advice and updates about their medical condition without them having to visit a hospital in person, and to notify individuals of potential infection hotspots in real-time so those areas can be avoided.
Social control, and the public information that can be spread via mobile devices, can be beneficial so long as we do not have a vaccine against the virus causing COVID-19. Without a vaccine, governments are left to resort to flatten the epidemiological curve, so as to help the healthcare industry not to be overwhelmed by a sudden increase in patients. And while lockdowns and social distancing measures can be effective to reduce the speed at which the virus spread, they come at an exorbitant economic cost and, therefore, at some time, will have to be phased out.
To limit the danger that there will be a rebound in infections once restrictions are lifted, it may be necessary for large scale diagnostic testing to identify those still infected and keep them in quarantine. In this approach, AI surveillance tools can be valuable. Large scale diagnostic testing is also necessary to fill in the data-gap that characterizes knowledge on the extent and fatality of the coronavirus. It is not known accurately how many people are in fact infected and how many are asymptomatic. A study in Science suggested that up to 86 percent of all infections may be undocumented. If this is accurate, then there are two important implications, one bad and one good news. One, the pandemic may easily rebound once lockdowns are lifted. Two, the virus may not be as lethal as is thought. In this regard, The Economist points out, If millions of people were infected weeks ago without dying, the virus must be less deadly than official data suggest.
The contribution of surveillance technology comes with one substantial risk: that once the outbreak is over, that erosion of data privacy would not be reversed, and that governments would continue to keep intrusive tabs ontheir populations. They can even potentially use the data obtained in the fight against COVID-19 for other purposes.
This risk of using AI in the fight against COVID-19 is perhaps reflective of the general risk in using AI. AI has both positive and negative impacts. There will always be trade-offs. For instance, if we consider the Sustainable Development Goals (SDGs) broadly, a recent survey published in Nature Communicationsemphasized that AI can enable the accomplishment of 134 targets across all the goals, but it may also inhibit 59 targets. AI can do good, but it can also do bad.
Take two more examples of how AI can do both good and bad at the same time. While NLP algorithms may warn against the possible outbreak of an epidemic by mining written reports on social media and online news, a recent study found that to train a standard NLP model to do this using Graphics Processing Unit (GPU) hardware, emits 626,155 pounds of CO2. This is five times more than an average car emits in its lifetime (120,000 lbs.). Another example is that AI-driven automation may raise productivity and firm efficiency, but may increase unemployment and poor-quality jobs (gigs), with higher poverty and inequality as outcomes.
Hence, the authors in Nature Communications recommend that the fast development of AI needs to be supported by the necessary regulatory insight and oversight for AI-based technologies to enable sustainable development. Failure to do so could result in gaps in transparency, safety, and ethical standards.
The key point is that we need to limit the potential adverse consequences of AI, and we need to do so through adequate governance of AI.
Having identified current efforts to harness AI against COVID-19, and having noted their promises, limitations, and potential pitfalls, it remains to identify the priorities for developing countries.
Developing countries are already having to deal with the economic fallout of the pandemic. As Hausmann argues, with revenues, trade, and investments dropping, developing countries would need to increase their indebtedness massively if they are to implement basic healthcare support and social distancing measures against the disease. They are losing policy space precisely when they need it the most. Therefore, prioritization of resources is vital.
Developing countries should prioritize their scarce resources on propping up their health sectors and providing social security to their citizens. In essence, they should not be investing their resources in AI in the hope of improving hospital efficiencies, or in finding a vaccine.
Although AI can be helpful in finding a vaccine, developing countries, and particularly those in Africa, are largely lagging in terms of AI research and development capability. As I document elsewhere, around 30 companies in three regions, North America, the EU, and China, perform the vast bulk of research, patenting (93%), as well as receives the bulk (more than 90 percent) of venture capital funding for AI.
This is not to say that developing countries have no interest in harnessing AI to find a vaccine they do, and this illustrates that such a vaccine is a global public good. Scott Barret has put forth the concept of a single-best effort public good, which can be applied to the search for a vaccine for COVID-19. In the case of a single-best effort public good, it can be produced by one or a few countries for the benefit of all countries. Thus, while developing countries should not be spending resources on finding pharmaceutical solutions to the crisis through AI, they should be part of a global coalition to harness the AI capabilities of high-income economies and China in this respect. What should be avoided is an uncoordinated response, an AI arms race between countries and regions, and uncertainty about the distribution of and access to such a vaccine.
Developing countries should not be spending resources on trying to find pharmaceutical solutions to the crisis, but should be part of a global coalition to harness the AI capabilities of high-income economies and China to find a vaccine and treatments
Developing countries should also partake in the gathering and building of large public databases on which to train AI. The costs of doing so are small, and the potential benefits, given the need for unbiased and representative data on the pandemic is high. It should be seen as an investment against future pandemics.
Finally, in terms of surveillance, AI, in combination with testing, may help developing countries to ease restrictions and lockdowns earlier. But as was discussed, this will come at the risk of compromised data privacy a price that may have to be paid for public health and the re-opening of economies.
How developing countries go about their AI-based surveillance and testing will be crucial. Developing country governments and the global community need to ensure adherence to the highest ethical standards and transparency. If they do not, then they may face the prospect that people will lose what little trust they had in government, which will, as Ienca and Vayena pointed out, make people less likely to follow public-health advice or recommendations and more likely to have poorer health outcomes.
For the developing countries of Africa, this makes it imperative that they ratify the African Unions Convention on Cyber Security and Personal Data Protection the Malabo Convention as soon as possible. On two countries have so far done this. Consistent with this convention, they should stop limiting internet access, internet censorship, and trying to restrict digital information flows.
Developing countries still face a substantial digital divide, and the worlds poorest region, Sub-Saharan Africa, face a particularly daunting challenge it currently contributes less than 1% of worlds digital knowledge production.The COVID-19 crisis, and its likely long-term consequences in terms of accelerating automation, online trade, reshoring as well as increasing the market power of large incumbent digital platforms, should spur on these countries to see the current crisis as an opportunity to speed up their digitalization, and to leverage from domestic and international sources the funding to invest in the long-run upgrading of data infrastructures and skills.
Note from the editors: Towards Data Science is a Medium publication primarily based on the study of data science and machine learning. We are not health professionals or epidemiologists, and the opinions of this article should not be interpreted as professional advice. To learn more about the coronavirus pandemic, you can click here.
Excerpt from:
Artificial Intelligence, COVID-19, and Developing Countries: Priorities and Trade-Offs - Elemental