A framework for grassroots research collaboration in machine learning and global health - Currin, Christopher; Asiedu, Mercy Nyamewaa; Fourie, Chris; Rosman, Benjamin; Turki, Houcemeddine; Lambebo Tonja, Atnafu; Abbott, Jade; Ajala, Marvellous; Adedayo, Sadiq Adewale; Emezue, Chris Chinenye; Machangara, Daphne; ICLR: International Conference on Learning Representations (1st Workshop on Machine Learning & Global Health, 2023)
Traditional top-down approaches for global health have historically failed to achieve social progress (Hoffman et al., 2015; Hoffman & Røttingen, 2015). Recently, however, a more holistic, multi-level approach termed One Health (OH) (Osterhaus et al., 2020) is being adopted. Several sets of 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. These exist despite the increasing accessibility to knowledge and digital collaborative 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 on the potential impact of the proposed framework for global health research collaborations.
published
yes
Keywords: conference paper