Data for Artificial Intelligence
Enabling data sharing for social benefit through data institutions
Objectives
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.
Resources
Team
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
Contributors
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
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.