Conceptual and normative approaches to AI governance for a global digital ecosystem supportive of the UN Sustainable Development Goals (SDGs).

AI governance is like one of those mythical creatures that everyone speaks of but which no one has seen. Sometimes, it is reduced to a list of shared principles such as transparency, non-discrimination, and sustainability; at other times, it is conflated with specific mechanisms for certification of algorithmic solutions or ways to protect the privacy of personal data. We suggest a conceptual and normative approach to AI governance in the context of a global digital public goods ecosystem to enable progress on the UN Sustainable Development Goals (SDGs). Conceptually, we propose rooting this approach in the human capability concept-what people are able to do and to be, and in a layered governance framework connecting the local to the global. Normatively, we suggest the following six irreducibles: a. human rights first; b. multi-stakeholder smart regulation; c. privacy and protection of personal data; d. a holistic approach to data use captured by the 3Ms-misuse of data, missed use of data and missing data; e. global collaboration ('digital cooperation'); f. basing governance more in practice, in particular, thinking separately and together about data and algorithms. Throughout the article, we use examples from the health domain particularly in the current context of the Covid-19 pandemic. We conclude by arguing that taking a distributed but coordinated global digital commons approach to the governance of AI is the best guarantee of citizen-centered and societally beneficial use of digital technologies for the SDGs.

Expanding the use of community health workers in urban settings: a potential strategy for progress towards universal health coverage.

Community health worker (CHW) programmes have been used for decades to improve access to health services in rural settings in low- and middle-income countries. With more than half of the world's population currently living in urban areas and this population expected to grow, equitable access to health services in urban areas is critically important. To understand the extent to which CHW programmes have been successfully deployed in low-income urban settings, we conducted a review of the literature between 2000 and 2018 to identify studies evaluating and describing CHW programmes implemented fully or partially in urban or peri-urban settings. We identified 32 peer-reviewed articles that met our inclusion criteria. Benefits have been documented in several urban settings in low- and middle-income countries including those to address TB/HIV, child health, maternal health and non-communicable diseases through a variety of study designs.

Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings?

The field of artificial intelligence (AI) has evolved considerably in the last 60 years. While there are now many AI applications that have been deployed in high-income country contexts, use in resource-poor settings remains relatively nascent. With a few notable exceptions, there are limited examples of AI being used in such settings. However, there are signs that this is changing. Several high-profile meetings have been convened in recent years to discuss the development and deployment of AI applications to reduce poverty and deliver a broad range of critical public services. We provide a general overview of AI and how it can be used to improve health outcomes in resource-poor settings. We also describe some of the current ethical debates around patient safety and privacy. Despite current challenges, AI holds tremendous promise for transforming the provision of healthcare services in resource-poor settings. Many health system hurdles in such settings could be overcome with the use of AI and other complementary emerging technologies. Further research and investments in the development of AI tools tailored to resource-poor settings will accelerate realising of the full potential of AI for improving global health.

Realizing the promise of The Partnership for Maternal, Newborn and Child Health.

Reflecting on Storeng and Béhague ("Lives in the balance": the politics of integration in the Partnership for Maternal, Newborn and Child Health. Health Policy and Planning Storeng and Béhague (2016).) historical ethnography of the Partnership for Maternal, Newborn and Child Health (PMNCH), this commentary provides a more current account of PMNCH's trajectory since its inception in 2005. It highlights PMNCH's distinct characteristics and how it is positioned to play an instrumental role in the current global health landscape.