Coordinators: Joyce Nakatumba-Nabende ([email protected]), Solomon Gizaw ([email protected]), Francesco Petruccione ([email protected]),
Researchers involved: Elena Gaura (Coventry University)
Artificial intelligence (AI) funding is critical for driving innovation and technological growth. Africa is making steps towards a faster uptake of AI, and the numerous AI-related investments and innovation are advancing across the continent. While only two countries have adopted AI strategies, there are several more that are taking steps towards defining AI policies. Effective Artificial Intelligence development across Africa requires a broad understanding of data and model governance and active community.
Recently, there has been a greater emphasis on amplifying the voices and representation of Africans and African contexts in defining the development, governance, and use of AI systems for our most pressing challenges, such as healthcare, agriculture, food security, language preservation and education. For example, a recent blog on open-source AI data sharing [1] highlights aspects around open data sharing and how to avoid the problem of unfair privatization and unfair exploitation of open-source AI. One critical issue that starts out is on open-source AI data sharing, namely balancing the tension between data privacy and data access and openness, usage, reuse, and management. However, in the corporate sector, these initiatives are usually undertaken with the assumption that Africa is a monolith and that once established, the consequences of such efforts can be duplicated across Africa.
This research will work on building an understanding of policy implications for AI data. This will be achieved through collaborations with expertise from the computing and legal departments through the organisation of policy hackathons, research papers and dialogue facilitation between academia, industry, public sector and government. The aim will be to develop policy recommendations and solutions that support data access and data processing required in AI research, development and application based on an understanding of the unique and specific contexts (social, cultural, economic, etc.) and to develop actionable and context-specific recommendations for private sector contributions in the development and governance of AI.