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 firstname.lastname@example.org. 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.
- 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.