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.

翻译：本研究探讨了农村地区从社区网络向社区数据的转型，重点关注如何结合数据池与数据合作社以实现数据正义并培育公正的人工智能生态系统。全球仍有27亿人口处于离线状态，特别是在全球南方地区，解决数据正义问题至关重要。尽管关于数据正义的讨论已扩展到经济维度，农村地区在数据集中的充分代表性仍面临挑战。本研究提出一种社区数据模型，该模型融合了数据池的简易性与数据合作社的结构化组织方式，旨在为"向善人工智能"生成本土化数据。该模型依托已被证实能有效促进数字包容的社区网络，建立集中式数据存储库，并通过开放数据原则确保可访问性。模型强调以社区需求为核心，优先关注本土知识、教育与传统实践，采用从试点项目起步的迭代推进方式。能力建设通过数字素养培训以及与教育机构、非政府组织的合作成为核心组成部分。法律与监管维度确保符合数据隐私法规。通过赋能农村社区掌控自身数据，该模型促进公平的数据访问与参与，并维系本土身份认同与知识体系。此方法可缓解农村地区数据创建面临的挑战，增强数据正义。社区数据模型通过提升数据质量与相关性，使农村地区能够受益于人工智能发展，从而为人工智能领域作出贡献。