  As machine learning and data science applications grow ever more prevalent,
there is an increased focus on data sharing and open data initiatives,
particularly in the context of the African continent. Many argue that data
sharing can support research and policy design to alleviate poverty,
inequality, and derivative effects in Africa. Despite the fact that the
datasets in question are often extracted from African communities,
conversations around the challenges of accessing and sharing African data are
too often driven by nonAfrican stakeholders. These perspectives frequently
employ a deficit narratives, often focusing on lack of education, training, and
technological resources in the continent as the leading causes of friction in
the data ecosystem. We argue that these narratives obfuscate and distort the
full complexity of the African data sharing landscape. In particular, we use
storytelling via fictional personas built from a series of interviews with
African data experts to complicate dominant narratives and to provide
counternarratives. Coupling these personas with research on data practices
within the continent, we identify recurring barriers to data sharing as well as
inequities in the distribution of data sharing benefits. In particular, we
discuss issues arising from power imbalances resulting from the legacies of
colonialism, ethno-centrism, and slavery, disinvestment in building trust, lack
of acknowledgement of historical and present-day extractive practices, and
Western-centric policies that are ill-suited to the African context. After
outlining these problems, we discuss avenues for addressing them when sharing
data generated in the continent.
