Google Research

A framework for grassroots research collaboration in machine learning and global health

  • Atnafu Lambebo Tonja
  • Benjamin Rosman
  • Chris Emezue
  • Chris Fourie
  • Christopher Brian Currin
  • Daphne Machangara
  • Houcemeddine Turki
  • Jade Abbott
  • Marvellous Ajala
  • Mennatullah Siam
  • Mercy Asiedu
  • Sadiq Adewale Adedayo
International Conference on Learning Representations (2023)


Traditional top-down approaches for global health have historically failed to achieve social progress (Hoffman et al., 2015; Hoffman & Røttingen, 2015). However, recently, a more holistic, multi-level approach, One Health (OH) (Osterhaus et al., 2020), is being adopted. Several challenges have been identified for the implementation of OH (dos S. Ribeiro et al., 2019), including policy and funding, education and training, and multi-actor, multi-domain, and multi-level collaborations. This is despite the increasing accessibility to knowledge and digital research tools through the internet. To address some of these challenges, we propose a general framework for grassroots community-based means of participatory research. Additionally, we present a specific roadmap to create a Machine Learning for Global Health community in Africa. The proposed framework aims to enable any small group of individuals with scarce resources to build and sustain an online community within approximately two years. We provide a discussion of the potential impact of the proposed framework on global health research collaborations.

Research Areas

Learn more about how we do research

We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work