Home Data for Artificial Intelligence Enabling data sharing for social benefit through data institutions

Data for Artificial Intelligence

Enabling data sharing for social benefit through data institutions

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This project explores novel data institutions that promote the safe, fair, and equitable sharing of data while empowering individuals and communities to enact their data rights, thus enabling an environment of trust for the development of responsible AI applications in service of the UN Sustainable Development Goals.

The project aims to build a framework for trustworthy data exchanges, inviting communities to play an active role in the data value chain by engaging various stakeholder organisations and local communities.

Community-based co-design of trustworthy data institutions for enabling the development of responsible AI applications.

The project intends to support data institutions by developing an ethical and fair standard of practices for the collection, storage, use, and sharing of data. Through an exploration of a selected use case on climate-induced migrations, the project is developing a trustworthy framework to ultimately be used by data institutions independent of use case context.

This project builds on a significant body of research commissioned by GPAI on both Data Trusts – exploring use cases about climate action, including climate migration; and Data Justice – developed a research agenda and practical guides with input from partner organisations from across Africa, Asia, Latin America, and Oceania, and proposed a roadmap for the responsible use of AI for the environment. This project continues this line of work, exploring practical contexts where data institutions and innovative data stewardship approaches (with a wider lens than data trusts) could enable an environment of trust for the development of responsible AI applications, specifically to help make a difference on climate-induced migration, with a primary focus on the Lake Chad Basin region.

The immediate impact of this project has been the value generated by mapping the data ecosystem and documenting the gaps and challenges which prevent establishing trust between those actors involved in the data chain in the data exchange system. Moreover, the project has identified opportunities to overcome these obstacles, including solutions proposed by local organisations and the associated strategies to implement them. As a result, several organisations have expressed interest in using the project’s framework in their various programs.


Thomas Hervé Mboa Nkoudou


Researcher in residence

Lama Saouma


AI Initiatives Lead

Stephanie King


Director of AI Initiatives

Advisory Committee

Teki Akuetteh Falconer

Alison Gillwald

Lee Tiedrich

Saiph Savage


Nga Minkala Alice

Ulrich Talla Wamba

David Alex Eto Mengom

Samuel Ngombi Oum

Christabella Ateh

Fomekong Félicien

Tirlamo Norbert Wirnkar

Aristide Donald BILOUNGA

Jafsia Élisée

Kengne Tagne

Yannick Ghislain

MAHAMAT Allamine

Collaborative opportunities with CEIMIA

Collaborating with CEIMA means contributing to the development of novel data institutions that enable data sharing for social benefits. For this project, we welcome two types of collaborations:

  • We are looking to join global initiatives around modelling and developing AI applications to share our work and insights on data institutions.
  • We welcome collaborations with organisations interested in testing the framework developed by the project. This could take the form of either using the data institutions framework in your own context and/or domain, or by providing general feedback on the existing framework based on relevant experience in the space.