Invitation DS4Society Webinar <> Beyond Dataset Creation: The Datafication Blind Spot in African AI Policy - 11 June 2026

Topic: **Beyond Dataset Creation: The Datafication Blind Spot in African AI Policy **Speaker/s: Jean Louis Fendji (University of Ngaoundéré, Cameroon) Date:https://forms.gle/Dz1Uhcou8PvhExGf611 June 2026 Time: 12:30 PM - 2:00 PM SAST

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Bio Jean Louis Fendji is an Associate Professor of Computer Science at the University of Ngaoundéré, Cameroon, where he leads the Centre for Research, Experimentation and Production (CREP) at the School of Chemical Engineering and Mineral Industries (EGCIM). He holds a PhD (Dr.-Ing.) in Computer Science from the University of Bremen, Germany.

His research leverages artificial intelligence, data justice, and optimization techniques to advance Sustainable Development Goals, with a focus on rural connectivity, precision agriculture, and digital inclusion for low-literacy populations speaking oral African languages. An Iso Lomso Fellow at the Stellenbosch Institute for Advanced Study (2024-2026) and a former fellow at the Hamburg Institute for Advanced Study (2025), Fendji is a Co-Principal Investigator for the EU Horizon project DIGITAfrica. He also contributes to national tech policy as a member of the ICT and Artificial Intelligence Commission within Cameroon’s Ministry of Scientific Research and Innovation.

Abstract

Africa contributes just 2% of global AI training data — yet the dominant institutional response remains the same: build more datasets. This presentation argues that this response, however well-intentioned, mistakes a structural problem for a logistical one. Drawing on a directed content analysis of nine African national and continental AI policy documents published between 2021 and 2025, we demonstrate that African AI strategies systematically address dataset creation while remaining blind to datafication — the continuous, infrastructure-embedded processes through which data is generated as a structural by-product of digital systems. The empirical finding is stark: across 288 coded segments, Dataset Creation Language outnumbers Datafication Language by 2.4:1, and not one of the nine strategies uses the word “datafication” or any direct conceptual equivalent. Seventy-six absence markers document the specific passages where datafication framing is missing despite contextual expectation. We examine what this blind spot looks like in practice — including the Ethiopian exception, where a DFL-dominant strategy plans IoT deployments across six sectors and 600 petabytes of sovereign AI storage, yet never connects these investments to AI training data production. The presentation closes with a datafication-first framework built on three reorienting principles and a tiered infrastructure model designed for Africa’s resource realities.

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