Essay by Stefania Milan and Emiliano Treré: “Quantification is central to the narration of the COVID-19 pandemic. Numbers determine the existence of the problem and affect our ability to care and contribute to relief efforts. Yet many communities at the margins, including many areas of the Global South, are virtually absent from this number-based narration of the pandemic. This essay builds on critical data studies to warn against the universalization of problems, narratives, and responses to the virus. To this end, it explores two types of data gaps and the corresponding “data poor.” The first gap concerns the data poverty perduring in low-income countries and jeopardizing their ability to adequately respond to the pandemic. The second affects vulnerable populations within a variety of geopolitical and socio-political contexts, whereby data poverty constitutes a dangerous form of invisibility which perpetuates various forms of inequality. But, even during the pandemic, the disempowered manage to create innovative forms of solidarity from below that partially mitigate the negative effects of their invisibility….(More)”.
Digital in the Time of the Coronavirus: Data Science and Technology as a Force for Inclusion
Blog by Aleem Walji: “Crises do not create inequity and fault lines in society, they expose them. The systems and structures that give rise to inequality and inequity are deep-rooted and powerful. In recent months, we have seen the coronavirus bring into high relief many social and economic vulnerabilities across the world. It is now clear that Hispanics and Blacks are even more vulnerable to Covid-19 because of underlying health conditions, more frequent exposure to the virus, and broken social safety nets. This trend will only accelerate as the virus gains a foothold in Africa, parts of Asia, and Latin America.
The impact of the virus in places where health systems are weak, poverty is high, and large numbers of people are immunocompromised could be devastating. How do we mitigate the medium-term and second-order effects of a pandemic that will shrink economic growth and exacerbate inequality? This year alone, more than 500 million people are expected to fall into poverty, mostly in Africa and Asia. To defeat a virus that does not respect geographic boundaries, it is urgent for public and private actors, philanthropies, and global development institutions to use every tool available to alleviate a global humanitarian emergency and attendant economic collapse.
Technology, data science, and digital readiness are crucial elements for an effective emergency response and foundational to sustain a long-term recovery. Already, scientists and researchers across the world are leveraging data and digital platforms to accelerate the development of a vaccine, fast-track clinical trials, and contact tracing using mobile-enabled tools. Sensors are collecting huge amounts of data, and machine learning algorithms are helping policymakers decide when to relax physical distancing and where to open the economy and for how long.
Access to reliable information for decisionmaking, however, is not evenly spread. High frequency, granular, and anonymized datasets are essential for public-health officials and community health workers to target interventions and reach vulnerable populations faster and at a lower cost. Equipped with reliable data, civic technologists can leverage tools like artificial intelligence and machine learning to flatten the curve of Covid-19 and also the curve of inequity and unequal access to services and support.
This will not happen on its own. Preventing a much deeper digital divide will require forward-leaning policymakers, far-sighted investors and grant makers, civic-minded tech innovators and businesses, and a robust, digitally savvy civil society to work collaboratively for social and economic inclusion. It will require political will and improved data governance to deploy digital platforms to serve populations furthest behind. It is in our collective interest to ensure the health and well-being of every segment of society. Digital inclusion is part of the solution.
There are certain pathways public, private and social actors can follow to leverage data science, digital tools, and platforms today….(More)”.
Trade-offs and considerations for the future: Innovation and the COVID-19 response
Essay by Benjamin Kumpf: “…Here are some of the relevant trade-offs I identified.
Rigour vs. Speed
How to best balance high-quality rigorous research and the need to gain actionable insights rapidly?
Responding to a pandemic requires working at pace, while investing in ongoing research and the cross-fertilization of disciplines. In our response, we witness the importance of strong networks with academia and DFID’s focus on high-quality research. In parallel, we invest in supporting partners with rapid data collection through methods such as phone surveys, field visits, onsite interviews where possible as well as big data analysis and more. For example, through the International Growth Centre, DFID has supported a Sierra Leone COVID-19 dashboard, providing real time data on current economic conditions and trends from phone–based surveys from 195 towns and villages across Sierra Leone. ….
Breadth vs. depth
How to best balance providing services to large proportions of populations in need, while addressing challenges of specific communities?
We are seeing emerging evidence that the virus and measures to prevent spread are disproportionately impacting marginalized communities and minorities. For example, in indigenous people are disproportionally affected by the virus in Brazil, Dalits are among the worst affected in India. In development and humanitarian contexts, it is paramount to guide innovation efforts with explicit values, including on the trade-off between scale and addressing last-mile challenges to leaveno–one behind. For example, to facilitate behaviour-change and embed insights from behavioural science and adaptive practices, DFID is supporting the Hygiene Hub, hosted at the London School for Hygiene and Tropical Medicine. The Hub provides free-of-charge advisory services to governments and non-governmental organizations working on COVID-19 related challenges in low and medium-income countries, balancing the need to reach large audiences and to design bespoke interventions for specific communities.
Exploration vs. adaptation
How to best diversify innovation efforts and investments betweensearching for local solution and adapting proven approaches? …
Adaptive vs. locally-led
How to best learn and adapt, while providing ownership to local players? …
Single-point solutions vs. systems-practices
How to advance specific tech and non-tech innovations that address urgent needs, while further improving existing systems? …
Supporting domestic innovators vs. strengthening local solutions and ecosystems
We need explicit conversations to ensure better transparency about this trade-off in innovation investments generally.…(More)”.
Digital inequalities 3.0: Emergent inequalities in the information age
Essay by Laura Robinson et al in FirstMonday: “Marking the 25th anniversary of the “digital divide,” we continue our metaphor of the digital inequality stack by mapping out the rapidly evolving nature of digital inequality using a broad lens. We tackle complex, and often unseen, inequalities spawned by the platform economy, automation, big data, algorithms, cybercrime, cybersafety, gaming, emotional well-being, assistive technologies, civic engagement, and mobility. These inequalities are woven throughout the digital inequality stack in many ways including differentiated access, use, consumption, literacies, skills, and production. While many users are competent prosumers who nimbly work within different layers of the stack, very few individuals are “full stack engineers” able to create or recreate digital devices, networks, and software platforms as pure producers. This new frontier of digital inequalities further differentiates digitally skilled creators from mere users. Therefore, we document emergent forms of inequality that radically diminish individuals’ agency and augment the power of technology creators, big tech, and other already powerful social actors whose dominance is increasing….(More)”
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)”.
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 [email protected]. 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.
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)”.