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As machine learning and data science applications grow ever more prevalent, the search for quality data has also increased. The Global South plays a central role in the global data landscape -- from being the target for “untapped” data mining, to contributing to the bulk of data labelling (ghost work) labour, to driving data sharing practices across the globe. And yet, the relationship between data science researchers, practitioners, NGO’s, and communities in the Global South and the rest of the global data science ecosystem is often marked with significant power asymmetries, histories and legacies of colonialism, lack of trust, and deficit narratives which frequently portrays the Global South in a negative light. There is insufficient understanding of the data practices of this hemisphere by those in the Global North, and in the process research for and by researchers in the Global South is also overlooked.
