Mapping citizen science contributions to the UN sustainable development goals

Paper by Dilek Frais: “The UN Sustainable Development Goals (SDGs) are a vision for achieving a sustainable future. Reliable, timely, comprehensive, and consistent data are critical for measuring progress towards, and ultimately achieving, the SDGs. Data from citizen science represent one new source of data that could be used for SDG reporting and monitoring. However, information is still lacking regarding the current and potential contributions of citizen science to the SDG indicator framework. Through a systematic review of the metadata and work plans of the 244 SDG indicators, as well as the identification of past and ongoing citizen science initiatives that could directly or indirectly provide data for these indicators, this paper presents an overview of where citizen science is already contributing and could contribute data to the SDG indicator framework.

The results demonstrate that citizen science is “already contributing” to the monitoring of 5 SDG indicators, and that citizen science “could contribute” to 76 indicators, which, together, equates to around 33%. Our analysis also shows that the greatest inputs from citizen science to the SDG framework relate to SDG 15 Life on Land, SDG 11 Sustainable Cities and Communities, SDG 3 Good Health and Wellbeing, and SDG 6 Clean Water and Sanitation. Realizing the full potential of citizen science requires demonstrating its value in the global data ecosystem, building partnerships around citizen science data to accelerate SDG progress, and leveraging investments to enhance its use and impact….(More)”.

UN Data Strategy

United Nations: “As structural UN reforms consolidate, we are focused on building the data, digital, technology and innovation capabilities that the UN needs to succeed in the 21st century. The Secretary General’s “Data Strategy for Action by Everyone, Everywhere” is our agenda for the data-driven transformation.

Data permeates all aspects of our work, and its power—harnessed responsibly—is critical to the global agendas we serve. The UN family’s footprint, expertise and connectedness create unique opportunities to advance global “data action” with insight, impact and integrity. To help unlock more potential, 50 UN entities jointly designed this Strategy as a comprehensive playbook for data-driven change based on global best practice…

Our strategy pursues a simple idea: we focus not on process, but on learning, iteratively, to deliver data use cases that add value for stakeholders based on our vision, outcomes and principles. Use cases – purposes for which data is used – already permeate our organization. We will systematically identify and deliver them through dedicated data action portfolios. While new capabilities will in part emerge through “learning by doing”, we will also strengthen organizational enablers to deliver on our vision, including shifts in people and culture, partnerships, data governance and technology….(More)”.

United Nations Data Strategy

Changing Citizen Behaviour: An Investigation on Nudge Approach in Developing Society

Paper by Dimas Budi Prasetyo: “It is widely explored that problems in developing society related to think and act logically and reflectively in a social context positively correlates with the cognition skill. In most developing societies, people are busy with problems that they face daily (i.e. working overtime), limits their cognitive capacity to properly process a social stimulus, which mostly asked their thoughtful response. Thus, a better design in social stimulus to tackle problematic behaviour, such as littering, to name a few, becomes more prominent. During the last decade, nudge has been famous for its subtle approach for behaviour change – however, there is relatively little known of the method applied in the developing society. The current article reviews the nudge approach to change human behaviour from two perspectives: cognitive science and consumer psychology. The article concludes that intervention using the nudge approach could be beneficial for current problematic behaviour…(More)”.

Selected Readings on AI for Development

By Dominik Baumann, Jeremy Pesner, Alexandra Shaw, Stefaan Verhulst, Michelle Winowatan, Andrew Young, Andrew J. Zahuranec

As part of an ongoing effort to build a knowledge base for the field of improving governance through technology, The GovLab publishes a series of Selected Readings, which provide an annotated and curated collection of recommended works on themes such as open data, data collaboration, and civic technology. 

In this edition, we explore selected literature on AI and Development. This piece was developed in the context of The GovLab’s collaboration with Agence Française de Développement (AFD) on the use of emerging technology for development. To suggest additional readings on this or any other topic, please email All our Selected Readings can be found here.

Context: In recent years, public discourse on artificial intelligence (AI) has focused on its potential for improving the way businesses, governments, and societies make (automated) decisions. Simultaneously, several AI initiatives have raised concerns about human rights, including the possibility of discrimination and privacy breaches. Between these two opposing perspectives is a discussion on how stakeholders can maximize the benefits of AI for society while minimizing the risks that might arise from the use of this technology.

While the majority of AI initiatives today come from the private sector, international development actors increasingly experiment with AI-enabled programs. These initiatives focus on, for example, climate modelling, urban mobility, and disease transmission. These early efforts demonstrate the promise of AI for supporting more efficient, targeted, and impactful development efforts. Yet, the intersection of AI and development remains nascent, and questions remain regarding how this emerging technology can deliver on its promise while mitigating risks to intended beneficiaries.

Readings are listed in alphabetical order.

2030Vision. AI and the Sustainable Development Goals: the State of Play

  • In broad language, this document for 2030Vision assesses AI research and initiatives and the Sustainable Development Goals (SDGs) to determine gaps and potential that can be further explored or scaled. 
  • It specifically reviews the current applications of AI in two SDG sectors, food/agriculture and healthcare.
  • The paper recommends enhancing multi-sector collaboration among businesses, governments, civil society, academia and others to ensure technology can best address the world’s most pressing challenges.

Andersen, Lindsey. Artificial Intelligence in International Development: Avoiding Ethical Pitfalls. Journal of Public & International Affairs (2019). 

  • Investigating the ethical implications of AI in the international development sector, the author argues that the involvement of many different stakeholders and AI-technology providers results in ethical issues concerning fairness and inclusion, transparency, explainability and accountability, data limitations, and privacy and security.
  • The author recommends the information communication technology for development (ICT4D) community adopt the Principles for Digital Development to ensure the ethical implementation of AI in international development projects.
  • The Principles of Digital Development include: 1) design with the user; 2) understand the ecosystem; 3) design for scale; 4) build for sustainability; 5) be data driven; 6) use open standards, open data, open source, and open innovation; and 7) reuse and improve.

Arun, Chinmayi. AI and the Global South: Designing for Other Worlds in Markus D. Dubber, Frank Pasquale, and Sunit Das (eds.), The Oxford Handbook of Ethics of AI, Oxford University Press, Forthcoming (2019).

  • This chapter interrogates the impact of AI’s application in the Global South and raises concerns about such initiatives.
  • Arun argues AI’s deployment in the Global South may result in discrimination, bias, oppression, exclusion, and bad design. She further argues it can be especially harmful to vulnerable communities in places that do not have strong respect for human rights.
  • The paper concludes by outlining the international human rights laws that can mitigate these risks. It stresses the importance of a human rights-centric, inclusive, empowering context-driven approach in the use of AI in the Global South.

Best, Michael. Artificial Intelligence (AI) for Development Series: Module on AI, Ethics and Society. International Telecommunications Union (2018). 

  • This working paper is intended to help ICT policymakers or regulators consider the ethical challenges that emerge within AI applications.
  • The author identifies a four-pronged framework of analysis (risks, rewards, connections, and key questions to consider) that can guide policymaking in the fields of: 1) livelihood and work; 2) diversity, non-discrimination and freedoms from bias; 3) data privacy and minimization; and 4) peace and security.
  • The paper also includes a table of policies and initiatives undertaken by national governments and tech companies around AI, along with the set of values (mentioned above) explicitly considered.

International Development Innovation Alliance (2019). Artificial Intelligence and International Development: An Introduction

  • Results for Development, a nonprofit organization working in the international development sector, developed a report in collaboration with the AI and Development Working Group within the International Development Innovation Alliance (IDIA). The report provides a brief overview of AI and how this technology may impact the international development sector.
  • The report provides examples of AI-powered applications and initiatives that support the SDGs, including eradicating hunger, promoting gender equality, and encouraging climate action.
  • It also provides a collection of supporting resources and case studies for development practitioners interested in using AI.

Paul, Amy, Craig Jolley, and Aubra Anthony. Reflecting the Past, Shaping the Future: Making AI Work for International Development. United States Agency for International Development (2018). 

  • This report outlines the potential of machine learning (ML) and artificial intelligence in supporting development strategy. It also details some of the common risks that can arise from the use of these technologies.
  • The document contains examples of ML and AI applications to support the development sector and recommends good practices in handling such technologies. 
  • It concludes by recommending broad, shared governance, using fair and balanced data, and ensuring local population and development practitioners remain involved in it.

Pincet, Arnaud, Shu Okabe, and Martin Pawelczyk. Linking Aid to the Sustainable Development Goals – a machine learning approach. OECD Development Co-operation Working Papers (2019). 

  • The authors apply ML and semantic analysis to data sourced from the OECD’s Creditor Reporting System to map aid funding to particular SDGs.
  • The researchers find “Good Health and Well-Being” as the most targeted SDG, what the researchers call the “SDG darling.”
  • The authors find that mapping relationships between the system and SDGs can help to ensure equitable funding across different goals.

Quinn, John, Vanessa Frias-Martinez, and Lakshminarayan Subramanian. Computational Sustainability and Artificial Intelligence in the Developing World. Association for the Advancement of Artificial Intelligence (2014). 

  • These researchers suggest three different areas—health, food security, and transportation—in which AI applications can uniquely benefit the developing world. The researchers argue the lack of technological infrastructure in these regions make AI especially useful and valuable, as it can efficiently analyze data and provide solutions.
  • It provides some examples of application within the three themes, including disease surveillance, identification of drought and agricultural trends, modeling of commuting patterns, and traffic congestion monitoring.

Smith, Matthew and Sujaya Neupane. Artificial intelligence and human development: toward a research agenda (2018).

  • The authors highlight potential beneficial applications for AI in a development context, including healthcare, agriculture, governance, education, and economic productivity.
  • They also discuss the risks and downsides of AI, which include the “black boxing” of algorithms, bias in decision making, potential for extreme surveillance, undermining democracy, potential for job and tax revenue loss, vulnerability to cybercrime, and unequal wealth gains towards the already-rich.
  • They recommend further research projects on these topics that are interdisciplinary, locally conducted, and designed to support practice and policy.

Tomašev, Nenad, et al. AI for social good: unlocking the opportunity for positive impact. Nature Communications (2020).

  • This paper takes stock of what the authors term the AI for Social Good movement (AI4SG), which “aims to establish interdisciplinary partnerships centred around AI applications towards SDGs.”  
  • Developed at a multidisciplinary expert seminar on the topic, the authors present 10 recommendations for creating successful AI4SG collaborations: “1) Expectations of what is possible with AI need to be well grounded. 2) There is value in simple solutions. 3) Applications of AI need to be inclusive and accessible, and reviewed at every stage for ethics and human rights compliance. 4) Goals and use cases should be clear and well-defined. 5) Deep, long-term partnerships are required to solve large problem successfully. 6) Planning needs to align incentives, and factor in the limitations of both communities. 7) Establishing and maintaining trust is key to overcoming organisational barriers. 8) Options for reducing the development cost of AI solutions should be explored. 9) Improving data readiness is key. 10) Data must be processed securely, with utmost respect for human rights and privacy.”

Vinuesa, Ricardo, et al. The role of artificial intelligence in achieving the Sustainable Development Goals. 

  • This report analyzes how AI can meet both the demands of some SDGs and also inhibit progress toward others. It highlights a critical research gap about the extent to which AI impacts sustainable development in the medium and long term. 
  • Through his analysis, Vinuesa claims AI has the potential to positively impact the environment, society, and the economy. However, AI can hinder these groups.
  • The authors recognize that although AI enables efficiency and productivity, it can also increase inequality and hinder achievements of the 2030 Agenda. Vinuesa and his co-authors suggest adequate policy formation and regulation are needed to ensure fast and equitable development of AI technologies that can address the SDGs. 

United Nations Education, Scientific and Cultural Organization (UNESCO) (2019). Artificial intelligence for Sustainable Development: Synthesis Report, Mobile Learning Week 2019

  • In this report, UNESCO assesses the findings from Mobile Learning Week (MLW) 2019. The three main conclusions were: 1) the world is facing a learning crisis; 2) education drives sustainable development; and 3) sustainable development can only be achieved if we harness the potential of AI. 
  • Questions around four major themes dominated the MLW 2019 sessions: 1) how to guarantee inclusive and equitable use of AI in education; 2) how to harness AI to improve learning; 3) how to increase skills development; and 4) how to ensure transparent and auditable use of education data. 
  • To move forward, UNESCO advocates for more international cooperation and stakeholder involvement, creation of education and AI standards, and development of national policies to address educational gaps and risks. 

Policy Priority Inference

Turing Institute: “…Policy Priority Inference builds on a behavioural computational model, taking into account the learning process of public officials, coordination problems, incomplete information, and imperfect governmental monitoring mechanisms. The approach is a unique mix of economic theory, behavioural economics, network science and agent-based modelling. The data that feeds the model for a specific country (or a sub-national unit, such as a state) includes measures of the country’s DIs and how they have moved over the years, specified government policy goals in relation to DIs, the quality of government monitoring of expenditure, and the quality of the country’s rule of law.

From these data alone – and, crucially, with no specific information on government expenditure, which is rarely made available – the model can infer the transformative resources a country has historically allocated to transform its SDGs, and assess the importance of SDG interlinkages between DIs. Importantly, it can also reveal where previously hidden inefficiencies lie.

How does it work? The researchers modelled the socioeconomic mechanisms of the policy-making process using agent-computing simulation. They created a simulator featuring an agent called “Government”, which makes decisions about how to allocate public expenditure, and agents called “Bureaucrats”, each of which is essentially a policy-maker linked to a single DI. If a Bureaucrat is allocated some resource, they will use a portion of it to improve their DI, with the rest lost to some degree of inefficiency (in reality, inefficiencies range from simple corruption to poor quality policies and inefficient government departments).

How much resource a Bureaucrat puts towards moving their DI depends on that agent’s experience: if becoming inefficient pays off, they’ll keep doing it. During the process, Government monitors the Bureaucrats, occasionally punishing inefficient ones, who may then improve their behaviour. In the model, a Bureaucrat’s chances of getting caught is linked to the quality of a government’s real-world monitoring of expenditure, and the extent to which they are punished is reflected in the strength of that country’s rule of law.

Diagram of the Policy Priority Inference model
Using data on a country or state’s development indicators and its governance, Policy Priority Inference techniques can model how a government and its policy-makers allocate “transformational resources” to reach their sustainable development goals.

When the historical movements of a country’s DIs are reproduced through the internal workings of the model, the researchers have a powerful proxy for the real-world relationships between government activity, the movement of DIs, and the effects of the interlinkages between DIs, all of which are unique to that country. “Once we can match outcomes, we can discern something that’s going on in reality. But the fact that the method is matching the dynamics of real-world development indicators is just one of multiple ways that we validate our results,” Guerrero notes. This proxy can then be used to project which policy areas should be prioritised in future to best achieve the government’s specified development goals, including predictions of likely timescales.

What’s more, in combination with techniques from evolutionary computation, the model can identify DIs that are linked to large positive spillover effects. These DIs are dubbed “accelerators”. Targeting government resources at such development accelerators fosters not only more rapid results, but also more generalised development…(More)”.

System-wide Roadmap for Innovating UN Data and Statistics

Roadmap by the United Nations System: “Since 2018, the Secretary-General has pursued an ambitious agenda to prepare the UN System for the challenges of the 21st century. In lockstep with other structural UN reforms, he has launched a portfolio of initiatives through the CEB to help transform system-wide approaches to new technologies, innovation and data. Driven by the urgency and ambition of the “Decade of Action”, these initiatives are designed to nurture cross-cutting capabilities the UN System will need to deliver better “for people and planet”. Unlocking data and harnessing the potential of statistics will be critical to the success of UN reform.

Recognizing that data are a strategic asset for the UN System, the UN Secretary-General’s overarching Data Strategy sets out a vision for a “data ecosystem that maximizes the value of our data assets for our organizations and the stakeholders we serve”, including high-level objectives, principles, core workstreams and concrete system-wide data initiatives. The strategy signals that improving how we collect, manage, use and share data should be a crosscutting strategic concern: Across all pillars of the UN System, across programmes and operations, and across all level of our organizations.

The System-wide Roadmap for Innovating UN Data and Statistics contributes to the overall objectives of the Data Strategy of the Secretary-General that constitutes a framework to support the Roadmap as a priority initiative. The two strategic plans converge around a vision that recognizes the power of data and stimulates the United Nations to embrace a more coherent and modern approach to data…(More)”.

The Responsible Data for Children (RD4C) Case Studies

Andrew Young at “This week, as part of the Responsible Data for Children initiative (RD4C), the GovLab and UNICEF launched a new case study series to provide insights on promising practice as well as barriers to realizing responsible data for children.

Drawing upon field-based research and established good practice, RD4C aims to highlight and support responsible handling of data for and about children; identify challenges and develop practical tools to assist practitioners in evaluating and addressing them; and encourage a broader discussion on actionable principles, insights, and approaches for responsible data management.

RD4C launched in October 2019 with the release of the RD4C Synthesis ReportSelected Readings, and the RD4C Principles: Purpose-Driven, People-Centric, Participatory, Protective of Children’s Rights, Proportional, Professionally Accountable, and Prevention of Harms Across the Data Lifecycle.

The RD4C Case Studies analyze data systems deployed in diverse country environments, with a focus on their alignment with the RD4C Principles. This week’s release includes case studies arising from field missions to Romania, Kenya, and Afghanistan in 2019. The data systems examined are:

Beyond Randomized Controlled Trials

Iqbal Dhaliwal, John Floretta & Sam Friedlander at SSIR: “…In its post-Nobel phase, one of J-PAL’s priorities is to unleash the treasure troves of big digital data in the hands of governments, nonprofits, and private firms. Primary data collection is by far the most time-, money-, and labor-intensive component of the vast majority of experiments that evaluate social policies. Randomized evaluations have been constrained by simple numbers: Some questions are just too big or expensive to answer. Leveraging administrative data has the potential to dramatically expand the types of questions we can ask and the experiments we can run, as well as implement quicker, less expensive, larger, and more reliable RCTs, an invaluable opportunity to scale up evidence-informed policymaking massively without dramatically increasing evaluation budgets.

Although administrative data hasn’t always been of the highest quality, recent advances have significantly increased the reliability and accuracy of GPS coordinates, biometrics, and digital methods of collection. But despite good intentions, many implementers—governments, businesses, and big NGOs—aren’t currently using the data they already collect on program participants and outcomes to improve anti-poverty programs and policies. This may be because they aren’t aware of its potential, don’t have the in-house technical capacity necessary to create use and privacy guidelines or analyze the data, or don’t have established partnerships with researchers who can collaborate to design innovative programs and run rigorous experiments to determine which are the most impactful. 

At J-PAL, we are leveraging this opportunity through a new global research initiative we are calling the “Innovations in Data and Experiments for Action” Initiative (IDEA). IDEA supports implementers to make their administrative data accessible, analyze it to improve decision-making, and partner with researchers in using this data to design innovative programs, evaluate impact through RCTs, and scale up successful ideas. IDEA will also build the capacity of governments and NGOs to conduct these types of activities with their own data in the future….(More)”.

Smart Village Technology

Book by Srikanta Patnaik, Siddhartha Sen and Magdi S. Mahmoud: “This book offers a transdisciplinary perspective on the concept of “smart villages” Written by an authoritative group of scholars, it discusses various aspects that are essential to fostering the development of successful smart villages. Presenting cutting-edge technologies, such as big data and the Internet-of-Things, and showing how they have been successfully applied to promote rural development, it also addresses important policy and sustainability issues. As such, this book offers a timely snapshot of the state-of-the-art in smart village research and practice….(More)”.

Google redraws the borders on maps depending on who’s looking

Greg Bensinger in the Washington Post: “For more than 70 years, India and Pakistan have waged sporadic and deadly skirmishes over control of the mountainous region of Kashmir. Tens of thousands have died in the conflict, including three just this month.

Both sides claim the Himalayan outpost as their own, but Web surfers in India could be forgiven for thinking the dispute is all but settled: The borders on Google’s online maps there display Kashmir as fully under Indian control. Elsewhere, users see the region’s snaking outlines as a dotted line, acknowledging the dispute.

Google’s corporate mission is “to organize the world’s information,” but it also bends it to its will. From Argentina to the United Kingdom to Iran, the world’s borders look different depending on where you’re viewing them from. That’s because Google — and other online mapmakers — simply change them.

With some 80 percent market share in mobile maps and over a billion users, Google Maps has an outsize impact on people’s perception of the world — from driving directions to restaurant reviews to naming attractions to adjudicating historical border wars.

And while maps are meant to bring order to the world, the Silicon Valley firm’s decision-making on maps is often shrouded in secrecy, even to some of those who work to shape its digital atlases every day. It is influenced not just by history and local laws, but also the shifting whims of diplomats, policymakers and its own executives, say people familiar with the matter, who asked not to be identified because they weren’t authorized to discuss internal processes….(More)”.