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
Sadiq Adewale Adedayo
International Conference on Learning Representations(2023)
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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.

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