Authors: Oleg Shakirov and Evgeniya Drozhashchikh*
The creation of the Global Partnership on Artificial Intelligence (GPAI) reflects the growing interest of states in AI technologies. The initiative, which brings together 14 countries and the European Union, will help participants establish practical cooperation and formulate common approaches to the development and implementation of AI. At the same time, it is a symptom of the growing technological rivalry in the world, primarily between the United States and China. Russia’s ability to interact with the GPAI may be limited for political reasons, but, from a practical point of view, cooperation would help the country implement its national AI strategy.
The Global Partnership on Artificial Intelligence (GPAI) was officially launched on June 15, 2020, at the initiative of the G7 countries alongside Australia, India, Mexico, New Zealand, South Korea, Singapore, Slovenia and the European Union. According to the Joint Statement from the Founding Members, the GPAI is an “international and multistakeholder initiative to guide the responsible development and use of AI, grounded in human rights, inclusion, diversity, innovation, and economic growth.”
In order to achieve this goal, GPAI members will look to bridge the gap between theory and practice by supporting both research and applied activities in AI. Cooperation will take place in the form of working groups that will be made up of leading experts from industry, civil society and the public and private sectors and will also involve international organizations. There will be four working groups in total, with each group focusing on a specific AI issue: responsible AI; data governance; the future of work; and innovation and commercialization. In acknowledgment of the current situation around the world, the partners also included the issue of using AI to overcome the socioeconomic effects of the novel coronavirus pandemic in the GPAI agenda.
In terms of organization, the GPAI’s work will be supported by a Secretariat to be hosted by the Organisation for Economic Co-Operation and Development (OECD) and Centres of Expertise – one each in Montreal and Paris.
To better understand how this structure came to be, it is useful to look at the history of the GPAI itself. The idea was first put forward by France and Canada in June 2018, when, on the eve of the G7 Summit, Justin Trudeau and Emmanuel Macron announced the signing of the Canada–France Statement on Artificial Intelligence, which called for the creation of an international group to study AI-related issues. By that time, both countries had already adopted their own national AI development strategies – Canada was actually the first country in the world to do so in March 2017. The two countries proposed a mandate for the international group, then known as the International Panel on Artificial Intelligence, at the G7 conference on artificial intelligence in late 2018. A declaration on the creation of the group was then made in May 2019, following a meeting of the G7 Ministers responsible for digital issues. The group was expected to be formally launched three months later at the G7 Summit in Biarritz, with other interested countries (such as India and New Zealand) joining.
However, the initiative did not receive the support of the United States wfithin the G7. Donald Trump and Emmanuel Macron were expected to announce the launch of the group at the end of the event, but the American delegation blocked the move. According to Lynne Parker, Deputy Chief Technology Officer at the White House, the United States is concerned that the group would slow down the development of AI technology and believes that it would duplicate the OECD’s work in the area. The originators of the idea to create the group (which received the name Global Partnership on Artificial Intelligence in Biarritz) clearly took this latter point into account, announcing that the initiative would be developed under the auspices of the OECD.
A Principled Partnership
Like other international structures, the OECD has started to pay greater attention to artificial intelligence in recent years, with its most important achievement in this area being the adoption of the Recommendation of the Council on Artificial Intelligence. Unlike other sets of principles on AI, the OECD’s recommendations were supported by the governments of all member countries, as well as by Argentina, Brazil, Colombia, Costa Rica, Peru and Romania, which made it the first international document of its kind. They were also used as the basis for the Global Partnership on Artificial Intelligence.
In accordance with the OECD recommendations, signatory countries will adhere to the following principles of AI development: promote AI technologies for inclusive growth, sustainable development and well-being; the priority of human-centred values and fairness throughout the life-cycle of AI systems; the transparency and (maximum possible) explainability of AI algorithms; the robustness, security and safety of AI systems; and the accountability of AI actors.
In addition to this, the document proposes that the following factors be taken into account when drafting national AI development strategies: investing in AI research and development; fostering a digital ecosystem for AI research and the practical implementation of AI technologies (including the necessary infrastructure); shaping national policies that allow for a smooth transition from theory to practice; building human capacity and preparing for labour market transformation; and expanding international cooperation in AI.
A few weeks after the OECD endorsement, the recommendations on AI were included as an annex to the G20 Ministerial Statement on Trade and Digital Economy dated July 9, 2019, albeit with slightly different wording. The principles thus received the support of Russia, China and India.
Within the OECD itself, the recommendations served as an impetus for the creation of the OECD AI Policy Observatory (OECD.AI), a platform for collecting and analysing information about AI and building dialogue with governments and other stakeholders. The platform will also be used within the framework of the Global Partnership on Artificial Intelligence.
Artificial Intelligence and Realpolitik
The decision of the United States to join the GPAI was likely motivated more by political reasons than anything else. In the run-up of the G7 Science and Technology Ministers’ Meeting in late May 2020 (where all participants, including the United States, officially announced the launch of the GPAI), Chief Technology Officer of the United States Michael Kratsios published an article in which he stated that democratic countries should unite in the development of AI on the basis of fundamental rights and shared values, rather than abuse AI to control their populations, which is what authoritarian regimes such as China do. According to Kratsios, it is democratic principles that unite the members of the GPAI. At the same time, Kratsios argues that the new coalition will not be a standard-setting or policy-making body, that is, it will not be a regulator in the field of AI.
The United States Strategic Approach to the People’s Republic of China published in May 2020 and the many practical steps that the American side has taken in recent years are a reflection of the tech war currently being waged between the United States and China. For example, the United States has taken a similar approach to the formation of new coalitions in the context of 5G technologies. In 2018–2019, the United States actively pushed the narrative that the solutions offered by Huawei for the creation of fifth-generation communications networks were not secure and convinced its allies to not work with Beijing. Thirty-two countries supported the recommendations put forward at the Prague 5G Security Conference in May 2019 (the Prague Proposals), which included ideas spread by the United States during its campaign against Huawei (for example, concerns about third countries influencing equipment suppliers).
The United States is not the only GPAI member that is concerned about China. Speaking back in January about the U.S. doubts regarding the Franco–Canadian initiative, Minister for Digital Affairs of France Cédric O noted, “If you don’t want a Chinese model in western countries, for instance, to use AI to control your population, then you need to set up some rules that must be common.” India’s participation in the GPAI is particularly telling, as the United States has been trying to involve India in containing China in recent years. The new association has brought together all the participants in the Quadrilateral Security Dialogue (Australia, India, the United States and Japan), which has always been a source of concern for Beijing, thus sending a very clear signal to the Chinese leadership.
The Prospects for Russia
The political logic that guides the United States when it comes to participating in the Global Partnership on Artificial Intelligence may very well extend to Russia. The Trump administration formally declared the return of great power competition in its 2017 National Security Strategy. In Washington, Russia and China are often referred to as the main rivals of the United States, promoting anti-American values.
When assessing the possibility of interaction between Russia and the GPAI, we need to look further than the political positions of the participants. According to the Joint Statement from the Founding Members, the GPAI is open to working with other interested countries and partners. In this regard, the obvious points of intersection between Russia and the new association may produce favourable conditions for practical cooperation in the future.
First of all, the GPAI members and Moscow rely on the same principles of AI development. Russia indirectly adopted the OECD recommendations on artificial intelligence when it approved the inclusion of the majority of their provisions in the Annex to the G20 Ministerial Statement on Trade and Digital Economy in 2019 and thus shares a common intention to ensure the responsible and human-centred development and use of artificial intelligence technologies. This does not mean that there will not be differences of opinion of specific issues, but, as we have already noted, in its current form, the activities of the GPAI will not be aimed at unifying the approaches of the participants.
Second, according to media reports, Russia is working to re-establish ties with the OECD. It is already helping the OECD with its website, periodically providing data on new legal documents that will create a framework for the development and implementation of AI that have been adopted or are being considered.
Third, the current development of the national AI ecosystem in Russia shows that the state, business and the scientific community are interested in the same topics that are on GPAI agenda. This is reflected in the National Strategy for the Development of Artificial Intelligence for the Period up to the Year 2030 adopted in October 2019 and the draft Federal Project on the Development of Artificial Intelligence as Part of the National Programme “Digital Economy of the Russian Federation.” Furthermore, following the adoption of the National Strategy last year, Russian tech companies set up an alliance for AI development in conjunction with the Russian Direct Investment Fund, which is very much in keeping with the multistakeholder approach adopted by the Global Partnership on Artificial Intelligence.
It would seem that politics is the main stumbling block when it comes to Russia’s possible participation in GPAI initiatives, for example, the organization’s clear anti-Chinese leaning or its members openly discrediting Russia’s approaches to the development of AI. That said, Russia has nothing to gain from politicizing the GPAI, since cooperation with the organization could help it achieve its own goals in artificial intelligence. What is more, we cannot rule out the possibility that the GPAI will be responsible in the future for developing unified AI rules and standards. It is in Russia’s interests to have its voice heard in this process to ensure that these standards do not turn into yet another dividing line.
*Evgeniya Drozhashchikh, Ph.D. Student in the Faculty of World Politics at Lomonosov Moscow State University, RIAC Expert
From our partner RIAC
From nanotechnology to solar power: Solutions to drought
While the drought has intensified in Iran and the country is facing water stress, various solutions from the use of solar power plants to the expansion of watershed management and nanotechnology are offered by experts and officials.
Iran is located in an arid and semi-arid region, and Iranians have long sought to make the most of water.
In recent years, the drought has intensified making water resources fragile and it can be said that we have reached water bankruptcy in Iran.
However, water stress will continue this fall (September 23-December 21), and the season is expected to be relatively hot and short of rain, according to Ahad Vazifeh, head of the national center for drought and crisis management.
In such a situation, officials and experts propose various solutions for optimal water management.
Alireza Qazizadeh, a water and environment expert, referring to 80 percent of the arid regions in the country, said that “Iran has one percent of the earth’s area and receives only 36 percent of renewable resources.
The country receives 250 mm of rainfall annually, which is about 400 billion cubic meters, considering 70 percent evaporation, there is only 130 billion cubic meters of renewable water and 13 billion cubic meters of input from border waters.”
Referring to 800 ml of average rainfall and 700 mm of global evaporation, he noted that 70 percent of rainfall in Iran occurs in only 25 percent of the country and only 25 percent rains in irrigation seasons.
Pointing to the need for 113 billion cubic meters of water in the current year (began on March 21), he stated that “of this amount, 102 billion is projected for agricultural use, 7 percent for drinking and 2 percent for industry, and at this point water stress occurs.
In 2001, 5.5 billion cubic meters of underground resources were withdrawn annually, and if we consider this amount as 20 years from that year until now, it means that we have withdrawn an equivalent of one year of water consumption from non-renewable resources, which is alarming.”
The use of unconventional water sources can be effective in controlling drought, such as rainwater or river runoff, desalinated water, municipal wastewater that can be reused by treatment, he concluded.
Rasoul Sarraf, the Faculty of Materials at Shahid Modarres University, suggests a different solution and states that “To solve ease water stress, we have no choice but to use nanotechnology and solar power plants.
Pointing to the sun as the main condition for solar power plant, and while pointing to 300 sunny days in the country, he said that at the Paris Convention, Iran was required to reduce emissions by 4 percent definitively and 8 percent conditionally, which will only be achieved by using solar power plants.
Hamidreza Zakizadeh, deputy director of watershed management at Tehran’s Department of Natural Resources and Watershed Management, believes that watershed management can at least reduce the effects of drought by managing floods and extracting water for farmers.
Amir Abbas Ahmadi, head of habitats and regional affairs of Tehran Department of Environment, also referring to the severe drought in Tehran, pointed to the need to develop a comprehensive plan for water management and said that it is necessary to cooperate with several responsible bodies and develop a comprehensive plan to control the situation.
He also emphasizes the need to control migration to the capital, construction, and the implementation of the Comprehensive Plan of Tehran city.
While various solutions are proposed by officials and experts to manage water and deal with drought, it is necessary for the related organizations to work together to manage the current situation.
Mohammad Reza Espahbod, an expert in groundwater resources, also suggested that while the country is dealing with severe drought due to improper withdrawal of groundwater and low rainfall, karst water resources can supply the whole water needed by the country, only if managed.
Iran is the fifth country in the world in terms of karst water resources, he stated.
Qanats can also come efficient to contain water scarcity due to relatively low cost, low evaporation rates, and not requiring technical knowledge, moreover, they proved sustainable being used in perpetuity without posing any damages to the environment.
According to the Ministry of Energy, about 36,300 qanats have been identified in Iran, which has been saturated with water for over 2,000 years.
In recent years, 3,800 qanats have been rehabilitated through watershed and aquifer management, and people who had migrated due to water scarcity have returned to their homes.
Water resources shrinking
Renewable water resources have decreased by 30 percent over the last four decades, while Iran’s population has increased by about 2.5 times, Qasem Taqizadeh, deputy minister of energy, said in June.
The current water year (started on September 23, 2020) has received the lowest rain in the past 52 years, so climate change and Iran’s arid region should become a common belief at all levels, he lamented.
A recent report by Nature Scientific Journal on Iran’s water crisis indicates that from 2002 to 2015, over 74 billion cubic meters have been extracted from aquifers, which is unprecedented and its revival takes thousands of years along with urgent action.
Three Iranian scientists studied 30 basins in the country and realized that the rate of aquifer depletion over a 14-year period has been about 74 billion cubic meters, which is recently published in Nature Scientific Journal.
Also, over-harvesting in 77 percent of Iran has led to more land subsidence and soil salinity. Research and statistics show that the average overdraft from the country’s aquifers was about 5.2 billion cubic meters per year.
Mohammad Darvish, head of the environment group in the UNESCO Chair on Social Health, has said that the situation of groundwater resources is worrisome.
From our partner Tehran Times
Technology and crime: A never-ending cat-and-mouse game
Is technology a good or bad thing? It depends on who you ask, as it is more about the way technology is used. Afterall, technology can be used by criminals but can also be used to catch criminals, creating a fascinating cat-and-mouse game.
Countless ways technology can be used for evil
The first spear was used to improve hunting and to defend from attacking beasts. However, it was also soon used against other humans; nuclear power is used to produce energy, but it was also used to annihilate whole cities. Looking at today’s news, we’ve learned that cryptocurrencies could be (and are) used as the preferred form of payments of ransomware since they provide an anonymous, reliable, and fast payment method for cybercriminals.
Similarly, secure phones are providing criminal rings with a fast and easy way to coordinate their rogue activities. The list could go on. Ultimately, all technological advancements can be used for good or evil. Indeed, technology is not inherently bad or good, it is its usage that makes the difference. After all, spears served well in preventing the extinction of humankind, nuclear power is used to generate energy, cryptocurrency is a promise to democratize finance, and mobile phones are the device of choice of billions of people daily (you too are probably reading this piece on a mobile).
However, what is new with respect to the past (recent and distant) is that technology is nowadays much more widespread, pervasive, and easier to manipulate than it was some time ago. Indeed, not all of us are experts in nuclear material, or willing and capable of effectively throwing a spear at someone else. But each of us is surrounded by, and uses, technology, with a sizeable part of users also capable of modifying that technology to better serve their purposes (think of computer scientists, programmers, coding kids – technology democratization).
This huge reservoir of people that are capable of using technology in a way that is different from what it was devised for, is not made of just ethical hackers: there can be black hats as well (that is, technology experts supporting evil usages of such technology). In technical terms, the attack vector and the security perimeter have dramatically expanded, leading to a scenario where technology can be easily exploited for rogue purposes by large cohorts of people that can attack some of the many assets that are nowadays vulnerable – the cybersecurity domain provides the best example for the depicted scenario.
Fast-paced innovation and unprecedented threats
What is more, is that technology developments will not stop. On the contrary, we are experiencing an exponentially fast pace in technology innovation, that resolves in less time between technology innovations cycles that, while improving our way of living, also pave the way for novel, unprecedented threats to materialize. For instance, the advent of quantum computers will make the majority of current encryption and digital signature methods useless and what was encrypted and signed in the past, exposed.
The tension between legitimate and illegitimate usages of technology is also heating up. For instance, there are discussions in the US and the EU about the need for the provider of ICT services to grant the decryption keys of future novel secure applications to law enforcement agencies should the need arise –a debatable measure.
However, technology is the very weapon we need to fight crime. Think of the use of Terahertz technology to discover the smuggling of drugs and explosives – the very same technology Qatar has successfully employed. Or the infiltration of mobile phone crime rings by law enforcement operators via high tech, ethical hacking (as it was the case for the EncroChat operation). And even if crime has shown the capability to infiltrate any sector of society, such as sports, where money can be laundered over digital networks and matches can be rigged and coordinated via chats, technology can help spot the anomalies of money transfer, and data science can spot anomalies in matches, and can therefore thwart such a crime – a recent United Nations-sponsored event, participated by the International Centre for Sport Security (ICSS) Qatar and the College of Science and Engineering (CSE) at Hamad Bin Khalifa University (HBKU) discussed the cited topic. In the end, the very same technology that is used by criminals is also used to fight crime itself.
Don’t get left behind
In the above-depicted cybersecurity cat-and-mouse game, the loser is the party that does not update its tools, does not plan, and does not evolve.
In particular, cybersecurity can help a country such as Qatar over two strategic dimensions: to better prevent/detect/react to the criminal usage of technology, as well as to advance robustly toward a knowledge-based economy and reinforce the country’s presence in the segment of high value-added services and products to fight crime.
In this context, a safe bet is to invest in education, for both governments and private citizens. On the one hand, only an educated workforce would be able to conceptualize/design/implement advanced cybersecurity tools and frameworks, as well as strategically frame the fight against crime. On the other hand, the same well-educated workforce will be able to spur innovation, create start-ups, produce novel high-skill products, and diversify the economy.
In this context, Qatar enjoys a head start, thanks to its huge investment in education over the last 20 years. In particular, at HBKU – part of Qatar Foundation – where we have been educating future generations.
CSE engages and leads in research disciplines of national and global importance. The college’s speciality divisions are firmly committed to excellence in graduate teaching and training of highly qualified students with entrepreneurial capacity.
For instance, the MS in Cybersecurity offered by CSE touches on the foundations of cryptocurrencies, while the PhD in Computer Science and Engineering, offering several majors (including cybersecurity), prepares future high-level decision-makers, researchers, and entrepreneurs in the ICT domain – the leaders who will be driving the digitalization of the economy and leading the techno-fight against crime.
Enhancing poverty measurement through big data
Authors: Jasmina Ernst and Ruhimat Soerakoesoemah*
Ending poverty in all its forms is the first of the 17 Sustainable Development Goals (SDGs). While significant progress to reduce poverty had been made at the global and regional levels by 2019, the Covid-19 pandemic has partly reversed this trend. A significant share of the population in South-East Asia still lacks access to basic needs such as health services, proper nutrition and housing, causing many children to suffer from malnutrition and treatable illnesses.
Delivering on the commitments of the 2030 Agenda for Sustainable Development and leaving no one behind requires monitoring of the SDG implementation trends. At the country level, national statistics offices (NSOs) are generally responsible for SDG data collection and reporting, using traditional data sources such as surveys, census and administrative data. However, as the availability of data for almost half of the SDG indicators (105 of 231) in South-East Asia is insufficient, NSOs are exploring alternative sources and methods, such as big data and machine learning, to address the data gaps. Currently, earth observation and mobile phone data receive most attention in the domain of poverty reporting. Both data sources can significantly reduce the cost of reporting, as the data collection is less time and resource intensive than for conventional data.
The NSOs of Thailand and the Philippines, with support from the Asian Development Bank, conducted a feasibility study on the use of earth observation data to predict poverty levels. In the study, an algorithm, convolutional neural nets, was pretrained on an ImageNet database to detect simple low-level features in images such as lines or curves. Following a transfer learning technique, the algorithm was then trained to predict the intensity of night lights from features in corresponding daytime satellite images. Afterwards income-based poverty levels were estimated using the same features that were found to predict night light intensity combined with nationwide survey data, register-based data, and geospatial information. The resulting machine learning models yielded an accuracy of up to 94 per cent in predicting the poverty categories of satellite images. Despite promising study results, scaling up the models and integrating big data and machine learning for poverty statistics and SDG reporting still face many challenges. Thus, NSOs need support to train their staff, gain continuous access to new datasets and expand their digital infrastructure.
Some support is available to NSOs for big data integration. The UN Committee of Experts on Big Data and Data Science for Official Statistics (UN-CEBD) oversees several task teams, including the UN Global Platform which has launched a cloud-service ecosystem to facilitate international collaboration with respect to big data. Two additional task teams focus on Big Data for the SDGs and Earth Observation data, providing technical guidance and trainings to NSOs. At the regional level, the weekly ESCAP Stats Café series provides a knowledge sharing platform for experiences related to the impact of COVID-19 on national statistical systems. The Stats Café includes multiple sessions dedicated to the use of alternative data sources for official statistics and the SDGs. Additionally, ESCAP has published policy briefs on the region’s practices in using non-traditional data sources for official statistics.
Mobile phone data can also be used to understand socioeconomic conditions in the absence of traditional statistics and to provide greater granularity and frequency for existing estimates. Call detail records coupled with airtime credit purchases, for instance, could be used to infer economic density, wealth or poverty levels, and to measure food consumption. An example can be found in poverty estimates for Vanuatu based on education, household characteristics and expenditure. These were generated by Pulse Lab Jakarta – a joint innovation facility associated with UN Global Pulse and the government of Indonesia.
Access to mobile phone data, however, remains a challenge. It requires long negotiations with mobile network operators, finding the most suitable data access model, ensuring data privacy and security, training the NSO staff and securing dedicated resources. The UN-CEBD – through the Task Team on Mobile Phone Data and ESCAP – supports NSOs in accessing and using mobile phone data through workshops, guides and the sharing of country experiences. BPS Statistics Indonesia, the Indonesian NSO, is exploring this data source for reporting on four SDG indicators and has been leading the regional efforts in South-East Asia. While several other NSOs in Asia and the Pacific can access mobile phone data or are negotiating access with mobile network operators, none of them have integrated it into poverty reporting.
As the interest and experience in the use of mobile phone data, satellite imagery and other alternative data sources for SDGs is growing among many South-East Asian NSOs, so is the need for training and capacity-building. Continuous knowledge exchange and collaboration is the best long-term strategy for NSOs and government agencies to track and alleviate poverty, and to measure the other 16 SDGs.
*Ruhimat Soerakoesoemah, Head, Sub-Regional Office for South-East Asia
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