Addressing Inequitable Openness in Licences for Sharing African Data and Datasets Through the Nwulite Obodo Open Data Licence
Record type:
This article examines the relationship between Standard Public Open Licences (SPOLs) and inequity in the artificial intelligence (AI) innovation ecosystem, focusing on how these licences affect access to and use of African datasets. While SPOLs are widely promoted as tools for democratising data access, they often apply uniform conditions to all users, disregarding disparities in infrastructure, capacity and socioeconomic context. As a result, SPOLs may unintentionally reinforce exclusion and enable extractive data practices that disadvantage communities contributing valuable datasets that they have preserved and curated through historically challenging conditions.
The study employs a desktop literature review of primary and secondary sources, complemented by analysis of specific case studies from the Masakhane Research Collective in Natural Language Processing and qualitative vignettes based on real-world experiences to identify inherent and systemic limitations of current SPOLs. The research shows how existing SPOLs, particularly those founded on copyright law, fail to accommodate the positionality of African and similarly situated users in the global data economy. In response, the article introduces the Nwulite Obodo Open Data Licence (NOODL Licence), a novel, tiered SPOL designed to foster equitable openness. NOODL differentiates conditions of use based on users’ geography and development context, incorporating benefit-sharing obligations and context-sensitive terms. It maintains the simplicity and legal clarity of existing SPOLs while addressing their inequities. By critically analysing the overlooked relationship between SPOLs and inequity, this article contributes a practical, context-aware licensing alternative that centres communities. While grounded in the African experience, the NOODL framework offers a replicable model for promoting fairness and inclusivity in global data governance and AI innovation.