Machine Intelligence in Africa: a survey
Allahsera Auguste Tapo, Ali Traore, Sidy Danioko, Hamidou Tembine
TL;DR
This survey surveys the state of machine intelligence across Africa, emphasizing the rise of audio-rich datasets and the need for culture-aware ethics to ensure inclusive MI deployment. It chronicles MI activities across 54 countries, highlighting research, industry, government actions, and uses in art, the informal economy, and small businesses. A key contribution is a critique of MI adoption indexes and the articulation of a multi-scale, culture-aware ethics framework to guide responsible MI development. The work argues for audio-centric approaches, local language datasets, and ethically-grounded policies to translate MI advances into tangible benefits for African populations, including those in informal sectors and rural areas. Overall, the article provides both a comprehensive landscape and a forward-looking agenda for ethically sound, locally relevant MI in Africa.
Abstract
In the last 5 years, the availability of large audio datasets in African countries has opened unlimited opportunities to build machine intelligence (MI) technologies that are closer to the people and speak, learn, understand, and do businesses in local languages, including for those who cannot read and write. Unfortunately, these audio datasets are not fully exploited by current MI tools, leaving several Africans out of MI business opportunities. Additionally, many state-of-the-art MI models are not culture-aware, and the ethics of their adoption indexes are questionable. The lack thereof is a major drawback in many applications in Africa. This paper summarizes recent developments in machine intelligence in Africa from a multi-layer multiscale and culture-aware ethics perspective, showcasing MI use cases in 54 African countries through 400 articles on MI research, industry, government actions, as well as uses in art, music, the informal economy, and small businesses in Africa. The survey also opens discussions on the reliability of MI rankings and indexes in the African continent as well as algorithmic definitions of unclear terms used in MI.