In 2014 the UN called for a data revolution to put the best available tools and methods to work in service of achieving the Sustainable Development Goals. Researchers at the World Bank have responded to that call by scouring the globe for the latest machine learning tools to transform our approach to tracking progress in the fight against poverty.
“Collecting household survey data on poverty is expensive and time-consuming, which means that policy makers are often making decisions based on old data,” said Asli Demirguc-Kunt, Director of Research at the World Bank. “Machine learning could drastically change the game, making poverty measurement cheaper and much closer to real-time.”
Machine learning is a field of computer science that allows computers to examine large bodies of data to identify patterns that data scientists would never find on their own.
Olivier Dupriez, a statistician at the World Bank, unveiled results from his and colleagues’ ongoing work on machine learning and poverty measurement at a recent Policy Research Talk. According to Dupriez, machine learning could make it possible to use a handful of easy-to-collect pieces of household information—such as whether a household has basic appliances or consumes a specific type of food—to predict the poverty status of a household. This would allow for poverty estimation based on simpler household survey instruments, and, hopefully, more effective policies to end poverty.
Data scientists have been developing the foundations of machine learning over the last half century, but its use has only become commonplace with the vast increases in computing power of the last decade. Today, machine learning is employed in everything from facial recognition technology to spam filters. In the case of poverty measurement, machine learning tools are employed to examine large national household survey datasets—which can be augmented with data from other sources, such as geospatial data—to develop models that can most effectively identify poor households.
From the outset, Dupriez and his colleagues faced a challenge: academia and the private sector have already created a multitude of machine learning tools, each with its own advantages and disadvantages. One tool may do a great job of predicting national poverty rates, but perform poorly when identifying specific households that fall below the poverty line.
Given the large number of options, the first stage of research has been exploratory. Dupriez and his colleagues have used large, comprehensive datasets on household poverty from Indonesia and Malawi to test four different approaches—with widely varying results.
The first of these applied 10 out-of-the-box machine learning algorithms to predict which households in Malawi and Indonesia are poor based on a handful of available variables. None of these algorithms proved to be a clear winner; different tools performed better on different metrics of poverty prediction. However, combining the results of these 10 out-of-the-box algorithms significantly improved predictive performance on most metrics.
The World Bank researchers also crowdsourced options through a global data science competition that attracted 6,000 submissions. The five winners from Portugal, Russia, China, and the Philippines faced the challenge of predicting household poverty status using anonymized datasets; they had no idea what country the data were from or whether specific households were classified as poor. Their models slightly outperformed the best of the 10 out-of-the-box algorithms used in the first approach.
Dupriez also employed an automated machine learning process, where the computer itself identifies the optimal way to build a model of poverty prediction. This approach requires a truly high-powered computer—in this case a computer with 32 processors worked for two days straight. The model produced via automated machine learning proved to be a disappointment, however, as it fell short on most quality metrics. But this may have been the result of the task assigned to it—the computer was directed to optimize only a single metric.
Instead, the most impressive results came from pairing experts and machines. Big tech companies like Google, Netflix, and Amazon have been innovating in the area of machine learning to produce content recommendation systems. Dupriez hired outside experts (Peter Bull and Casey Fitpatrick of DrivenData), who deployed an algorithm similar to the one Google uses for its Play Store. This algorithm produced excellent results on almost all metrics, making it a strong contender for additional investigation despite its complexity.
While the results so far are promising, the research is still in an early phase, with much more work needed to turn machine learning into a practical tool that can make poverty measurement faster and cheaper. Globally, data scientists need to commit to openness around data, software, and methods to make this a reality.
“The reason that our outside experts were able to build the best performing model is that they know a lot about what model works with what kind of data and what kind of challenge,” said Dupriez. “We need to develop a library of reproducible scripts and open data where researchers can find any kind of machine learning model, no matter who created it, so that we as a community can apply it to the most pressing challenges the world faces.”
Digital Child’s Play: protecting children from the impacts of AI
Artificial intelligence has been used in products targeting children for several years, but legislation protecting them from the potential impacts of the technology is still in its infancy. Ahead of a global forum on AI for children, UN News spoke to two UN Children’s Fund (UNICEF) experts about the need for improved policy protection.
Children are already interacting with AI technologies in many different ways: they are embedded in toys, virtual assistants, video games, and adaptive learning software. Their impact on children’s lives is profound, yet UNICEF found that, when it comes to AI policies and practices, children’s rights are an afterthought, at best.
In response, the UN children’s agency has developed draft Policy Guidance on AI for Children to promote children’s rights, and raise awareness of how AI systems can uphold or undermine these rights.
Conor Lennon from UN News asked Jasmina Byrne, Policy Chief at the UNICEF Global Insights team, and Steven Vosloo, a UNICEF data, research and policy specialist, about the importance of putting children at the centre of AI-related policies.
AI Technology will fundamentally change society.
Steven Vosloo At UNICEF we saw that AI was a very hot topic, and something that would fundamentally change society and the economy, particularly for the coming generations. But when we looked at national AI strategies, and corporate policies and guidelines, we realized that not enough attention was being paid to children, and to how AI impacts them.
So, we began an extensive consultation process, speaking to experts around the world, and almost 250 children, in five countries. That process led to our draft guidance document and, after we released it, we invited governments, organizations and companies to pilot it. We’re developing case studies around the guidance, so that we can share the lessons learned.
Jasmina Byrne AI has been in development for many decades. It is neither harmful nor benevolent on its own. It’s the application of these technologies that makes them either beneficial or harmful.
There are many positive applications of AI that can be used in in education for personalized learning. It can be used in healthcare, language simulation and processing, and it is being used to support children with disabilities.
And we use it at UNICEF. For example, it helps us to predict the spread of disease, and improve poverty estimations. But there are also many risks that are associated with the use of AI technologies.
Children interact with digital technologies all the time, but they’re not aware, and many adults are not aware, that many of the toys or platforms they use are powered by artificial intelligence. That’s why we felt that there has to be a special consideration given to children and because of their special vulnerabilities.
Privacy and the profit motive
Steven Vosloo The AI could be using natural language processing to understand words and instructions, and so it’s collecting a lot of data from that child, including intimate conversations, and that data is being stored in the cloud, often on commercial servers. So, there are privacy concerns.
We also know of instances where these types of toys were hacked, and they were banned in Germany, because they were considered to be safe enough.
Around a third of all online users are children. We often find that younger children are using social media platforms or video sharing platforms that weren’t designed with them in mind.
They are often designed for maximum engagement, and are built on a certain level of profiling based on data sets that may not represent children.
Predictive analytics and profiling are particularly relevant when dealing with children: AI may profile children in a way that puts them in a certain bucket, and this may determine what kind of educational opportunities they have in the future, or what benefits parents can access for children. So, the AI is not just impacting them today, but it could set their whole life course on a different direction.
Jasmina Byrne Last year this was big news in the UK. The Government used an algorithm to predict the final grades of high schoolers. And because the data that was input in the algorithms was skewed towards children from private schools, their results were really appalling, and they really discriminated against a lot of children who were from minority communities. So, they had to abandon that system.
That’s just one example of how, if algorithms are based on data that is biased, it can actually have a really negative consequences for children.
‘It’s a digital life now’
Steven Vosloo We really hope that our recommendations will filter down to the people who are actually writing the code. The policy guidance has been aimed at a broad audience, from the governments and policymakers who are increasingly setting strategies and beginning to think about regulating AI, and the private sector that it often develops these AI systems.
We do see competing interests: the decisions around AI systems often have to balance a profit incentive versus an ethical one. What we advocate for is a commitment to responsible AI that comes from the top: not just at the level of the data scientist or software developer, from top management and senior government ministers.
Jasmina Byrne The data footprint that children leave by using digital technology is commercialized and used by third parties for their own profit and for their own gain. They’re often targeted by ads that are not really appropriate for them. This is something that we’ve been really closely following and monitoring.
However, I would say that there is now more political appetite to address these issues, and we are working to put get them on the agenda of policymakers.
Governments need to think and puts children at the centre of all their policy-making around frontier digital technologies. If we don’t think about them and their needs. Then we are really missing great opportunities.
Steven Vosloo The Scottish Government released their AI strategy in March and they officially adopted the UNICEF policy guidance on AI for children. And part of that was because the government as a whole has adopted the Convention on the Rights of the Child into law. Children’s lives are not really online or offline anymore. And it’s a digital life now.
How digital technology and innovation can help protect the planet
As a thick haze descended over New Delhi last month, air quality monitors across the Indian capital began to paint a grim picture.
The smoke, fed by the seasonal burning of crops in northern India, was causing levels of the toxic particle PM 2.5 to spike, a trend residents could track in real time on the Global Environment Monitoring System for Air (GEMS Air) website.
By early November, GEMS Air showed that concentrations of PM 2.5 outside New Delhi’s iconic India Gate were ‘hazardous’ to human health. In an industrial area north of the Indian capital, the air was 50 times more polluted.
GEMS Air is one of several new digital tools used by the United Nations Environment Programme (UNEP) to track the state of the environment in real time at the global, national and local levels. In the years to come, a digital ecosystem of data platforms will be crucial to helping the world understand and combat a host of environmental hazards, from air pollution to methane emissions, say experts.
“Various private and public sector actors are harnessing data and digital technologies to accelerate global environmental action and fundamentally disrupt business as usual,” says David Jensen, the coordinator of UNEP’s digital transformation task force.
“These partnerships warrant the attention of the international community as they can contribute to systemic change at an unprecedented speed and scale.”
The world is facing what United Nations Secretary-General António Guterres has called a triple planetary crisis of climate change, pollution and biodiversity loss. Experts say averting those catastrophes and achieving the Sustainable Development Goals will require fundamentally transforming the global economy within a decade. It’s a task that would normally take generations. But a range of data and digital technologies are sweeping the planet with the potential to promote major structural transformations that will enhance environmental sustainability, climate action, nature protection and pollution prevention.
A new age
UNEP is contributing to that charge through a new programme on Digital Transformation and by co-championing the Coalition for Digital Environmental Sustainability as part of the Secretary-General’s Digital Cooperation Roadmap.
UNEP studies show that for 68 per cent of the environment-related Sustainable Development Goal indicators, there is not enough data to assess progress. The digital initiatives leverage technology to halt the decline of the planet and accelerate sustainable finance, products, services, and lifestyles.
GEMS air was among the first of those programmes. Run by UNEP and Swiss technology company IQAir, it is the largest air pollution network in the world, covering some 5,000 cities. In 2020, over 50 million users accessed the platform and its data is being streamed into digital billboards to alert people about air quality risks in real time. In the future, the program aims to extend this capability directly into mobile phone health applications.
Building on lessons learned from GEMS Air, UNEP has developed three other lighthouse digital platforms to showcase the power of data and digital technologies, including cloud computing, earth observation and artificial intelligence.
One is the Freshwater Ecosystem Explorer, which provides a detailed look at the state of lakes and rivers in every country on Earth.
The fruit of a partnership between UNEP, the European Commission’s Joint Research Centre and Google Earth Engine, it provides free and open data on permanent and seasonal surface waters, reservoirs, wetlands and mangroves.
“It is presented in a policy-friendly way so that citizens and governments can easily assess what is actually happening to the world’s freshwater resources,” says Stuart Crane, a UNEP freshwater expert. “That helps countries track their progress towards the achievement of Sustainable Development Goal Target 6.6.”
Data can be visualized using geospatial maps with accompanying informational graphics and downloaded at national, sub-national and river basin scales. Data are updated annually and depict long-term trends as well as annual and monthly records on freshwater coverage.
Combating climate change
UNEP is also using data-driven decision making to drive deep reductions in methane emissions through the International Methane Emissions Observatory (IMEO). Methane is a potent greenhouse gas, responsible for at least a quarter of today’s global warming.
The observatory is designed to shine a light on the origins of methane emissions by collecting data from various sources, including satellites, ground-based sensors, corporate reporting and scientific studies.
The Global Methane Assessment published by UNEP and the Climate and Clean Air Coalition (CCAC) found that cutting human-caused methane by 45 per cent this decade would avoid nearly 0.3°C of global warming by the 2040s, and help prevent 255,000 premature deaths, 775,000 asthma-related hospital visits, and 26 million tonnes of crop losses globally.
“The International Methane Emissions Observatory supports partners and institutions working on methane emissions reduction to scale-up action to the levels needed to avoid the worst impacts of climate change,” says Manfredi Caltagirone, a UNEP methane emissions expert.
Through the Oil and Gas Methane Partnership 2.0, the methane observatory works with petroleum companies to improve the accuracy and transparency of methane emissions reporting. Current member companies report assets covering over 30 per cent of oil and gas production globally. It also works with the scientific community to fund studies that provide robust, publicly available data.
UNEP is also backing the United Nations Biodiversity Lab 2.0, a free, open-source platform that features data and more than 400 maps highlighting the extent of nature, the effects of climate change, and the scale of human development. Such spatial data help decision-makers put nature at the heart of sustainable development by allowing them to visualize the natural systems that hold back natural disasters, store planet-warming gasses, like carbon dioxide, and provide food and water to billions.
More than 61 countries have accessed data on the UN Biodiversity Lab as part of their national reporting to the Convention on Biological Diversity, an international accord designed to safeguard wildlife and nature. Version 2.0 of the lab was launched in October 2021 as a partnership between UNDP, UNEP’s World Conservation Monitoring Centre, the Convention on Biodiversity Secretariat and Impact Observatory.
All of UNEP’s digital platforms are being federated into UNEP’s World Environment Situation Room, a digital ecosystem of data and analytics allowing users to monitor progress against key environmental Sustainable Development Goals and multi-lateral agreements at the global, regional and national levels.
“The technical ability to measure global environmental change—almost in real time—is essential for effective decision making,” says Jensen.
“It will have game-changing implications if this data can be streamed into the algorithms and platforms of the digital economy, where it can prompt users to make the personal changes so necessary to preserving the natural world and achieving net zero.”
Housing needs, the Internet and cyberspace at the forefront in the UK and Italy
Modern construction methods and smart technology can revolutionise the building process and the way we live.
Population growth and demographic changes have led to a global housing shortage. According to research carried out by the Heriot-Watt University National Housing Federation and by the Homeless Charity Crisis Organisation, the UK will face a shortage of four million housing units by the end of 2031. This means that approximately 340,000 new housing units will need to be built each year. The houses built shall meet the demands of home automation and increasing environmental constraints.
Traditional building technology is unlikely to meet this demand. It is relatively expensive and too slow in fulfilling the necessary procedures and complying with all rules and regulations. Furthermore, the quality and capabilities of traditional construction methods are also limited. The only solution is modular production based on the principles of factory automation. This solution uses cordless and battery-free controls and sensors to perfectly integrate with home automation.
Modular buildings are based on a combination of construction methods called Modern Method of Construction (MMC). They include the use of panelling systems and components, such as roof and floor boxes, precast concrete foundation components, prefabricated wiring, mechanical engineering composites and innovative technologies.
With the opening of several factories, the UK has started to use the MMC to build prefabricated and fully equipped houses in modular form, which can be loaded onto trucks for transport across the country. This type of on-site assembly enables the house to be completed in days rather than months, thus reducing costs significantly. Modular buildings have become popular in Europe. In Italy, a pioneering company is the RI Group of Trepuzzi (Lecce), which is also operating in the fields of logistics and services and building health care facilities, field hospitals and public offices, which are cost-effective and quick to construct.
The impact of modular construction is expected to be significant and factories producing up to five thousand houses per year could become the best builders in the sector.
The construction standards of these new technology houses are higher than those of traditional houses. Thanks to better insulation, the electricity bill could be only half that of a traditional house.
Modular houses have kitchens and bathrooms, and are equipped with power and lighting via power cables, which are also modular, and wireless controls, in addition to the increasingly important network and telecommunications infrastructure.
Structural and modular wiring are derived from commercial electrical and industrial installations to ensure efficient and minimal electrical installation work. As technology changes, this standard installation is adaptable and offers a high degree of flexibility.
Experience in industrial and commercial construction shows that traditional fixtures are labour-intensive, rather rigid and still expensive. In contrast, on-site prefabricated modular cabling and the IDC system combined with wireless controllers and sensors can be fully installed at low cost. These are proven technologies and are moving from commercial to domestic use scenarios.
With the help of CAD support for modular cabling, all power cables are laid in the ceiling or wall space. The installation of wireless energy harvesting equipment simplifies the installation process as no switches and duct installation are required. For the first electrical fixing through the wall, the cable takes less time because there is no need to coordinate the position of the switch with the wall bolts. The level of dependency of on-site installation activities has also been reduced. Sensors, switches and wireless energy harvesting controls can be installed anywhere in the building, even in hard-to-reach areas.
After installation, the principle of energy harvesting will be used. Switches and sensors are powered by the surrounding environment and there is no need to replace old batteries and other maintenance equipment. Moreover, this flexibility and this reliability enable the system to be expanded at any time.
The modular construction technology enables it to adapt to various types of houses and meet the needs of today’s life through flexible shapes and various exterior decorations. This is not exactly the same as the old prefabricated houses, “granted” in Italy to earthquake victims who have been waiting for years for a decent, civilised home.
By providing a range of traditional and modern exterior decorative panels, the roofline can also be customised to suit local customs and architecture.
Through the combination of innovative product technology and good design, the aim of the smart home is to provide security and comfort. The usual requirement is to place the light switch and dimmer (or potentiometer) in the most convenient place. Driven by the kinetic energy collected by the switch itself, they can be placed anywhere.
They do not require wiring, but can send wireless signals to the receiver inside or near lights or DIN-rail mounts (German Institute for Standardisation). In addition, there is no need to use batteries and no need to replace them. This saves all the inconvenience and environmental risks that can be caused by replacing batteries.
Since this type of equipment has reached a wide range of applications, lighting and home entertainment will choose battery-free products. Besides controlling brightness and colour, self-powered switches can also be used to control sound systems or blinds. A key application of the smart home is the switch that can turn off/on devices that do not use traditional electricity when leaving or coming back home.
Energy harvesting technology also supports other sensor-based applications. For example, self-powered sensors can be wirelessly connected to an intruder alarm. Furthermore, by installing light-activated touch sensors on windows, lighting and heating can be turned off when no one is at home.
Another source of energy is the temperature difference between the heating radiator and the surrounding environment. For example, this energy harvesting enables a self-powered heating valve to perform heating control via a room temperature controller according to specific conditions.
From factories to offices, from multifunctional buildings to smart homes, wireless energy harvesting technology has been tested in approximately one million buildings worldwide. Most sensors, switches and other self-powered energy-harvesting devices can communicate at a distance of up to 30 metres in a building and meet the EnOcean international wireless standard, which encrypts messages below 1 GHz by sending a short message.
There are also some self-powered devices that integrate EnOcean energy harvesting technology and can communicate directly with the lights via the well-known Bluetooth or Zigbee (wireless communication standard based on the IEEE 802.15.4 specification, maintained by the ZigBee Alliance). This makes it possible to use green, battery-free switches and solar sensors to flexibly control other applications, such as LED lights or speakers.
Now that wireless sensors for energy harvesting can frame data at home, it will be a huge step forward to aggregate information and perform useful analysis. They process data through the Internet of Things (IoT), which refers to the path in technological development whereby, through the Internet, potentially every object of everyday life can acquire its own identity in cyberspace. As mentioned above, the IoT is based on the idea of “smart” items which are interconnected to exchange the information they possess, collect and/or process.
It also uses Artificial Intelligence (AI) to keep track of living patterns and activities in modular homes. Energy analysis is an application that can currently help homeowners further reduce energy consumption through AI.
Looking to the future, the combination of the IoT and AI will bring many benefits. Geographical data, weather and climate information, as well as activities, water and energy consumption and other factors will be very useful for planners, building organisations, builders and landlords.
Perceived architecture represents the next generation of sustainable building systems. Smart buildings will soon be able to integrate the IoT devices on their own, as well as generate large amounts of information and use it to optimise buildings. This provides a whole new dimension to the service and to the business and home economics model.
This is particularly relevant for the ageing population, as these smart technologies can radically change the lifestyles of the elderly people and their families. They are expected to bring transformative benefits in terms of health and well-being.
The key elements of such a home include smart, non-invasive and safe and secure connections with friends, family members, general practitioners, nurses and health care professionals, involving the care of residents. Technology based on battery-free sensors connected to the IoT will help prevent accidents at home, resulting from kitchens utensils and overflowing toilets, etc., and keep up with residents’ interactions with healthcare professionals.
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