# Narratives and Counternarratives on Data Sharing in Africa

arXiv: https://arxiv.org/abs/2103.01168 · HTML: https://mlsanity.com/paper/2103.01168/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2021.

## Built on by (most cited first, 14 of 14)

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- [Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements](https://mlsanity.com/paper/2306.10043/) (2023)
- [The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotation](https://mlsanity.com/paper/2602.11318/) (2026)
- [Program Targeting with Machine Learning and Mobile Phone Data: Evidence from an Anti-Poverty Intervention in Afghanistan](https://mlsanity.com/paper/2206.11400/) (2022)
- [Data Ethics in the Era of Healthcare Artificial Intelligence in Africa: An Ubuntu Philosophy Perspective](https://mlsanity.com/paper/2406.10121/) (2024)
- [Enriching Moral Perspectives on AI: Concepts of Trust amongst Africans](https://mlsanity.com/paper/2508.14116/) (2025)
- [A Non-Expert's Introduction to Data Ethics for Mathematicians](https://mlsanity.com/paper/2201.07794/) (2022)
- [Contextualizing Artificially Intelligent Morality: A Meta-Ethnography of Top-Down, Bottom-Up, and Hybrid Models for Theoretical and Applied Ethics in Artificial Intelligence](https://mlsanity.com/paper/2204.07612/) (2022)
- [Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in Senegal](https://mlsanity.com/paper/2211.06277/) (2022)
- [Sparse Attention Regression Network Based Soil Fertility Prediction With Ummaso](https://mlsanity.com/paper/2404.10274/) (2024)
- [A Capabilities Approach to Studying Bias and Harm in Language Technologies](https://mlsanity.com/paper/2411.04298/) (2024)

## Abstract (arXiv)

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.