Advanced Technologies for Sustainable Development


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It is clear that mere access to infrastructure is not enough, no matter how high-speed or sophisticated. How broadband applications and services and data are used – and for which purposes – is becoming vitally important for development, and for achieving the SDGs and the promise to “leave no one behind”.

Technologies themselves are usually amoral – technology can be used for good or bad and is given moral purpose, depending on the uses and goals to which it is put. Undoubtedly, there are ethical issues and social dilemmas that can arise with poorly considered uses of these technologies. As Professor Yuval Noah Harari notes in his book, “Homo Deus: A Brief History of Tomorrow” (1), technologies and medicine almost always begin by “helping people and saving them from falling below the norm, but the same tools and know-how can then be used to surpass the norm” (Harari, 2017). “Once you achieve a momentous breakthrough, you cannot restrict its use

to healing and completely forbid using it for upgrading” (Harari, 2017) and purposes other than those originally foreseen.

It is paramount to capitalize on the positive potential of broadband, ICTs and new technologies, to achieve the most extensive and far-reaching beneficial impact for as many people as possible. Indeed, the moral purpose of technologies is becoming ever more important, given the far-reaching scale and predictive power now made possible through new and emerging technologies, such as big data and AI.

There is no universally agreed definition of artificial intelligence. “AI” is a term of art

that has been used for at least forty years, to apply to any number of processes. Historically, this term has been applied where machines imitate thinking or behavior that people associate with human intelligence (such as learning, speech and problem solving). AI refers to the theory and development of computer systems able to perform tasks that normally require human intelligence (such as visual perception or decision-making), and comprises a rich set of sub-disciplines and methods with different functions, including visual recognition, perception, speech recognition and dialogue, decisions, planning and robotics, among others.

New families of AI algorithms now make it possible to obtain actionable insights automatically and at scale. Accompanying these developments in AI is big data. According to Maaroof (2015), “big data is not just [about] data—no matter how big or different it is considered to be; big data is first and foremost ‘about’ the analytics, the tools and methods that are used to yield insights, the frameworks, standards, stakeholders involved and then, knowledge” (2). Big data opens up opportunities towards a potential shift towards information-rich and more informed policy-making.

As discussed in Section 3.1 on NBPs, the Open Data Institute has suggested that public data should be recognized as an infrastructure asset (3). Policies that discuss data infrastructure in the government typically focus on management of data assets (collection, access, reuse, sharing, preservation, security) and data governance (ownership, funding) (4) (World Bank, 2018). This implies that data is an asset, much like any other infrastructure asset. Table 4 from the World Bank depicts some of the different types of data collected by different players in the newly emerging data

ecosystem, while Viewpoint 22 by UN Global Pulse explores how big – and better – data and AI can be used for sustainable development.

Table 4: Different Types of Data

Source: World Bank.

Viewpoint 22: Better Data for Doing Good – Using Big Data & AI for Sustainable Development

4.1 Digital Technologies for Education

Globalization, new technologies, migration, and environmental and political challenges are transforming labour markets and creating demand for new skills and knowledge for work, citizenship and managing personal lives. Digital skills are fast becoming vital, in addition to basic literacy and numeracy. Digital skills can themselves be further broken down into three categories – the basic digital literacy needed for all workers, consumers and citizens in a digital society; the advanced ICT skills (coding, computer science and engineering) which are needed to develop innovative ICT products and services; and e-business skills or the specific know-how needed for digital entrepreneurship (5). According to GSMA, 29% of global mobile users use their phones to access information to support their education, or that of the children or relatives.

The report, “Technologies & the future of learning and Education for All” (UNICEF & UNESCO, 2018 (6)) identifies various core functions enabled by new digital technologies, including: enhancing the role of teachers as facilitators; delivering engaging quality content; enabling learners to acquire new skills; assessment and certification; efficient delivery; improved administration; and effective learning. Viewpoint 23 describes the opportunities provided by digital technologies for enhanced learning and education. Indeed, digital technologies are most effective when they support teachers and accompany students in their learning processes. Online learning resources (such as language and translation platforms, for example) can help supplement and reinforce learning, as well as digital tutors, learning and language academies, curricular playlists and intelligent virtual reality. Digital technologies can and are being used to support in different needs individual students, classes, teachers and learning establishments (primary, secondary and higher education), as well as students with specific learning difficulties.

Viewpoint 23: The Opportunities Provided by Digital Technologies for Learning & Education

For example, according to some estimates, some 10% of the population at any time

may have dyslexia, a neurological learning disability that affects reading and writing but does not affect general intelligence. Children with dyslexia can learn coping strategies to deal with its negative effects. Unfortunately, in many cases, dyslexia may be detected too late for effective intervention. Change Dyslexia is a Spanish project that uses AI cutting-edge scientifically based computer games, such as Dytective Test and DytectiveU, to screen and support dyslexic children at largescale (7).

“Indonesia Belajar” (Indonesia’s Learning) is a digital education programme focusing

on increasing digital literacy in Indonesia. It uses technology to make education more accessible for children across the country. In 2017, five major Indonesia Belajar programs were launched which have supported some 2,500 teachers and 50,000 students to date. These programs leverage VR and AR to educate communities about digital literacy and improve community learning, with long-term benefits for remote areas across Indonesia. Viewpoint 24 describes the use of digital technologies for education in Africa.

Viewpoint 24: Digital Technologies for Education in Africa

4.2 Digital Technologies for Health

Digital technologies can be used for a range of purposes to promote positive health outcomes and to support health systems to cope with their growing disease and

cost burdens (11). Mobile-based products in health insurance and remittances can help expand coverage while reducing waste and inefficiencies in health system financing. They can enable organizations and health managers to collect data on dashboards, providing real-time evidence for decision-making. New technologies such as 5G and AI can provide new applications for the e-health applications (remote surgery, remote diagnostics etc.).

According to GSMA, 26% of global mobile users access services that help them to improve or monitor their health and/or the health of their family on a mobile phone.

The sources and quantities of health data from mobile devices, Internet searches and wearables are growing. Growth in computing power and predictive analytics is enabling the study and use of vast amounts of information that reveal patterns, trends and associations, thanks in part to big data. For example, mobile data records and big data have been used to track the migration of people with Ebola in Sierra Leone, the spread of dengue fever in Pakistan (12) and cholera in Haiti. With regards to AI, IBM has outfitted Watson, its “cognitive computing” platform, to tackle multiple challenges in healthcare (13).

Governments, health authorities and other stakeholders are moving to capitalize on these advantages. For example, the number of mHealth products and services has doubled in the past five years in LMICs (14), and there are now over 165,000 mobile applications for health services (15). Fifty-nine percent of patients in the LMICs are using mHealth applications and services versus 35% in high-income countries (16). Globally, 44% of mobile users have seen a medical professional using a mobile device during diagnosis or treatment (17), and 86% of clinicians believe that health applications can facilitate diagnosis (18). There is clearly a need to meet growing demand through digital health solutions. Viewpoint 25 explores the impact of digital health on Non-Communicable Diseases (NCDs) for Universal Health Coverage (UHC). Viewpoint 26 explores mobile’s role in driving behavioural change for underserved communities, while Viewpoint 27 explores the work of the Carlos Slim Foundation in Mexico.

Viewpoint 25: Promoting Digital Financial Inclusion

Viewpoint 26: Mobile’s role in driving behavioural change for underserved communities

Viewpoint 27: Accelerating the Implementation of Digital Health as a Public Policy in Mexico

4.3 Digital Technologies for the Environment

The evidence is mounting to suggest that considerable challenges are emerging with respect to our natural environment. The most common environmental threats are loss and degradation of natural habitat, but unsustainable exploitation, invasive species and pollution are also proving major threats (20). Digital and sensor technologies offer opportunities to monitor the environment and wildlife populations accurately.

Big data analysis can be used to help update old-fashioned reporting of animal populations. For example, WWF and the Zoological Society of London (ZSL) have developed the Living Planet Index (LPI) and Database as a measure of the state of global biological diversity based on population trends of vertebrate species from around the world, with time-series data for over 19,500 populations of more than 4,000 mammal, bird, fish, reptile and amphibian species. ICTs and sensor technologies can play a big role and offer huge potential for game-changing solutions. With big data and technologies, the time for companies and governments underplaying deforestation, wildlife trade, poaching or illegal fishing is over. AI can be used to help boost protection and resilience of natural systems.

Remote sensing plays an important role in planning, monitoring, and evaluating WWF’s work on the ground and has enabled WWF to monitor the developments of extractive industries in socially and ecologically-sensitive areas, including World Heritage sites. The Natural Capital Project uses remote-sensing-based natural capital assessment to guide jurisdictional development planning, mapping supply risk for corporate sourcing decision, and helping conservation organizations target investments in forest restoration.

ICTs can be used extensively to observe, monitor, track and protect our terrestrial wildlife from poachers as well as other destructive activities. WWF is working with governments and enforcement agencies to explore, fund, and test a wide range

of technologies becoming available for wildlife conservation – from drones and wildlife tracking to radar, thermal cameras and gunshot detectors. WWF has found that unmanned aerial vehicles or UAVs function best as ‘reactionary eyes’ in the sky. WWF is testing civilian-grade UAVs for conservation applications with plans to rigorously test the technology in protected areas in Malawi, Namibia and Zimbabwe.

Thermal imaging cameras have been used by anti-poaching teams in Lake Nakuru National Park and in the Maasai Mara Game Reserve to increase the chances of catching poachers hunting antelope and rhinos by over 60%. Anti-poaching teams have also been able to achieve all this with smaller numbers of patrol teams. Wildlife management using tracking collars can also help conservation efforts – for elephants and lions in Kenya.

However, the use of these new technologies is open to question – they can be used

to protect the environment, as well as to enable humankind to exploit natural resources more effectively. For example, the same tracking technologies can be used to monitor natural populations of tunafish in the oceans or lions on land, or they can be used to hunt the same animals or to stimulate and attract public interest (21).

Big data can help generate and analyze a greater number of on-the-ground observations. For example, the University of Minnesota’s Lion Project has deployed 225 camera traps across 1,125 square kilometers in the Serengeti National Park in Tanzania to evaluate spatial and temporal dynamics since 2010 to produce 1.2 million sets of pictures by 2013. Members of the general public classified the images via a citizen-science website (22). The project applies an algorithm to aggregate the classifications to investigate multi-species dynamics in an intact ecosystem (23).

With regards to marine life, the Republic of Indonesia has partnered with Global Fishing Watch (a partnership between Google, Oceana and SkyTruth) to deliver Vessel Monitoring System (VMS) data for Indonesian flagged fishing vessels in a publicly-available platform. The project aims to promote transparency in the fishing industry (24) as to which ocean areas are fished and for which species of fish.


  1. Harari, Y.N., 2016. Homo Deus: A brief history of tomorrow. Random House.
  2. “Big Data and the 2030 Agenda for Sustainable Development”, Dr. A Maaroof (2015), available at:
  4. “Data-Driven Development”, ICT4 Development Report 2018, World Bank (forthcoming).
  5. The European Centre for the Development of Vocational Training (Cedefop) in “Skills, Qualifications and Jobs in the EU: The Making of a Perfect Match?” (Cedefop, Thessaloniki, 2015).
  6. HLCP Discussion Paper, “Technologies and the Future of Learning & Education for All”, prepared under the leadership of UNICEF and UNESCO, with support from other UN agencies.
  11. “Digital Health: A Call for Government Leadership and Cooperation between ICT and Health”, Report of the Working Group on Health, February 2017, available at:
  12. Wesolowski, A., Qureshi, T., Boni, M.F., Sundsøy, P.R., Johansson, M.A., Rasheed, S.B., Engø-Monsen, K. and Buckee, C.O., 2015. Impact of human mobility on the emergence of dengue epidemics in Pakistan. Proceedings of the National Academy of Sciences, 112(38), pp.11887-11892.
  14. GSMA, “The Mobile Economy 2015”, available from
  15. IMS Institute for Healthcare Informatics, “Patient Adoption of mHealth: Use, Evidence and Remaining Barriers to Mainstream Acceptance, 2015”, available at:
  16. PriceWaterhouseCoopers, “Emerging mHealth: Paths for growth 2012”, available from
  17. Mobile Ecosystem Forum, “Global mHealth & Wearables Report 2015”, available at:
  18. PriceWaterhouseCoopers, “Top Health Industry Issues of 2015”, available from
  19. The Mobile Economy 2017, GSMAi
  20. WWF’s Living Planet Report 2016, available at:
  21. Consider the international outcry surrounding the killing of Cecil the lion in Zimbabwe, the famous lion tagged and tracked by an Oxford University study.
  23. Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna, available at: