From Community Network to Community Data: Towards Combining Data Pool and Data Cooperative for Data Justice in Rural Areas

• URL: http://arxiv.org/abs/2503.05950v1
• Date: Fri, 07 Mar 2025 21:41:01 GMT
• Title: From Community Network to Community Data: Towards Combining Data Pool and Data Cooperative for Data Justice in Rural Areas
• Authors: Jean Louis Fendji Kedieng Ebongue,
• Abstract summary: This study explores the shift from community networks (CNs) to community data in rural areas.<n>It focuses on combining data pools and data cooperatives to achieve data justice and foster and a just AI ecosystem.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study explores the shift from community networks (CNs) to community data in rural areas, focusing on combining data pools and data cooperatives to achieve data justice and foster and a just AI ecosystem. With 2.7 billion people still offline, especially in the Global South, addressing data justice is critical. While discussions related to data justice have evolved to include economic dimensions, rural areas still struggle with the challenge of being adequately represented in the datasets. This study investigates a Community Data Model (CDM) that integrates the simplicity of data pools with the structured organization of data cooperatives to generate local data for AI for good. CDM leverages CNs, which have proven effective in promoting digital inclusion, to establish a centralized data repository, ensuring accessibility through open data principles. The model emphasizes community needs, prioritizing local knowledge, education, and traditional practices, with an iterative approach starting from pilot projects. Capacity building is a core component of digital literacy training and partnership with educational institutions and NGOs. The legal and regulatory dimension ensures compliance with data privacy laws. By empowering rural communities to control and manage their data, the CDM fosters equitable access and participation and sustains local identity and knowledge. This approach can mitigate the challenges of data creation in rural areas and enhance data justice. CDM can contribute to AI by improving data quality and relevance, enabling rural areas to benefit from AI advancements.

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