关于非洲数据共享的叙述和反驳
Narratives and Counternarratives on Data Sharing in Africa
摘要：
随着机器学习和数据科学应用越来越普遍，人们越来越关注数据共享和开放数据举措，特别是在非洲大陆。许多人认为，数据共享可以支持研究和政策设计，以减轻非洲的贫困、不平等和衍生效应。尽管有关数据集往往是从非洲社区提取的，但围绕获取和共享非洲数据的挑战的对话往往是由非洲以外的利益攸关方推动的。这些观点经常采用赤字叙述，通常侧重于非洲大陆缺乏教育，培训和技术资源，这是数据生态系统摩擦的主要原因。我们认为，这些叙述混淆和扭曲的非洲数据共享景观的全部复杂性。特别是，我们通过对非洲数据专家的一系列采访建立的虚构人物角色来讲述故事，以使主导叙事复杂化，并提供反叙事。将这些人物角色与对非洲大陆数据实践的研究相结合，我们发现了数据共享的经常性障碍以及数据共享利益分配的不公平。特别是，我们讨论了殖民主义，种族中心主义和奴隶制的遗产造成的权力不平衡，在建立信任，缺乏对历史和当今采掘业的承认，以及不适合非洲背景的西方中心政策的撤资所产生的问题。在概述了这些问题之后，我们讨论了在共享非洲大陆生成的数据时解决这些问题的途径。
英文摘要：
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.
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